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yuntian-deng

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ak-paper-selection

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2305.03837

2023-05-05T20:35:42Z

Mask The Bias: Improving Domain-Adaptive Generalization of CTC-based ASR with Internal Language Model Estimation

End-to-end ASR models trained on large amount of data tend to be implicitly biased towards language semantics of the training data. Internal language model estimation (ILME) has been proposed to mitigate this bias for autoregressive models such as attention-based encoder-decoder and RNN-T. Typically, ILME is performed by modularizing the acoustic and language components of the model architecture, and eliminating the acoustic input to perform log-linear interpolation with the text-only posterior. However, for CTC-based ASR, it is not as straightforward to decouple the model into such acoustic and language components, as CTC log-posteriors are computed in a non-autoregressive manner. In this work, we propose a novel ILME technique for CTC-based ASR models. Our method iteratively masks the audio timesteps to estimate a pseudo log-likelihood of the internal LM by accumulating log-posteriors for only the masked timesteps. Extensive evaluation across multiple out-of-domain datasets reveals that the proposed approach improves WER by up to 9.8% and OOV F1-score by up to 24.6% relative to Shallow Fusion, when only text data from target domain is available. In the case of zero-shot domain adaptation, with no access to any target domain data, we demonstrate that removing the source domain bias with ILME can still outperform Shallow Fusion to improve WER by up to 9.3% relative.

2305.03846

2023-05-05T20:53:36Z

Data-Free Learning of Reduced-Order Kinematics

Physical systems ranging from elastic bodies to kinematic linkages are defined on high-dimensional configuration spaces, yet their typical low-energy configurations are concentrated on much lower-dimensional subspaces. This work addresses the challenge of identifying such subspaces automatically: given as input an energy function for a high-dimensional system, we produce a low-dimensional map whose image parameterizes a diverse yet low-energy submanifold of configurations. The only additional input needed is a single seed configuration for the system to initialize our procedure; no dataset of trajectories is required. We represent subspaces as neural networks that map a low-dimensional latent vector to the full configuration space, and propose a training scheme to fit network parameters to any system of interest. This formulation is effective across a very general range of physical systems; our experiments demonstrate not only nonlinear and very low-dimensional elastic body and cloth subspaces, but also more general systems like colliding rigid bodies and linkages. We briefly explore applications built on this formulation, including manipulation, latent interpolation, and sampling.

2305.04825

2023-05-05T11:10:48Z

NewsQuote: A Dataset Built on Quote Extraction and Attribution for Expert Recommendation in Fact-Checking

To enhance the ability to find credible evidence in news articles, we propose a novel task of expert recommendation, which aims to identify trustworthy experts on a specific news topic. To achieve the aim, we describe the construction of a novel NewsQuote dataset consisting of 24,031 quote-speaker pairs that appeared on a COVID-19 news corpus. We demonstrate an automatic pipeline for speaker and quote extraction via a BERT-based Question Answering model. Then, we formulate expert recommendations as document retrieval task by retrieving relevant quotes first as an intermediate step for expert identification, and expert retrieval by directly retrieving sources based on the probability of a query conditional on a candidate expert. Experimental results on NewsQuote show that document retrieval is more effective in identifying relevant experts for a given news topic compared to expert retrieval

2305.09783

2023-05-05T14:33:16Z

Deep Learning for Solving and Estimating Dynamic Macro-Finance Models

We develop a methodology that utilizes deep learning to simultaneously solve and estimate canonical continuous-time general equilibrium models in financial economics. We illustrate our method in two examples: (1) industrial dynamics of firms and (2) macroeconomic models with financial frictions. Through these applications, we illustrate the advantages of our method: generality, simultaneous solution and estimation, leveraging the state-of-art machine-learning techniques, and handling large state space. The method is versatile and can be applied to a vast variety of problems.

2305.03565

2023-05-05T14:16:29Z

The geometry of financial institutions -- Wasserstein clustering of financial data

The increasing availability of granular and big data on various objects of interest has made it necessary to develop methods for condensing this information into a representative and intelligible map. Financial regulation is a field that exemplifies this need, as regulators require diverse and often highly granular data from financial institutions to monitor and assess their activities. However, processing and analyzing such data can be a daunting task, especially given the challenges of dealing with missing values and identifying clusters based on specific features. To address these challenges, we propose a variant of Lloyd's algorithm that applies to probability distributions and uses generalized Wasserstein barycenters to construct a metric space which represents given data on various objects in condensed form. By applying our method to the financial regulation context, we demonstrate its usefulness in dealing with the specific challenges faced by regulators in this domain. We believe that our approach can also be applied more generally to other fields where large and complex data sets need to be represented in concise form.

2305.03608

2023-05-05T15:11:28Z

On the Optimality, Stability, and Feasibility of Control Barrier Functions: An Adaptive Learning-Based Approach

Safety has been a critical issue for the deployment of learning-based approaches in real-world applications. To address this issue, control barrier function (CBF) and its variants have attracted extensive attention for safety-critical control. However, due to the myopic one-step nature of CBF and the lack of principled methods to design the class-$\mathcal{K}$ functions, there are still fundamental limitations of current CBFs: optimality, stability, and feasibility. In this paper, we proposed a novel and unified approach to address these limitations with Adaptive Multi-step Control Barrier Function (AM-CBF), where we parameterize the class-$\mathcal{K}$ function by a neural network and train it together with the reinforcement learning policy. Moreover, to mitigate the myopic nature, we propose a novel \textit{multi-step training and single-step execution} paradigm to make CBF farsighted while the execution remains solving a single-step convex quadratic program. Our method is evaluated on the first and second-order systems in various scenarios, where our approach outperforms the conventional CBF both qualitatively and quantitatively.

2305.03874

2023-05-05T23:24:24Z

Learning Stochastic Dynamical System via Flow Map Operator

We present a numerical framework for learning unknown stochastic dynamical systems using measurement data. Termed stochastic flow map learning (sFML), the new framework is an extension of flow map learning (FML) that was developed for learning deterministic dynamical systems. For learning stochastic systems, we define a stochastic flow map that is a superposition of two sub-flow maps: a deterministic sub-map and a stochastic sub-map. The stochastic training data are used to construct the deterministic sub-map first, followed by the stochastic sub-map. The deterministic sub-map takes the form of residual network (ResNet), similar to the work of FML for deterministic systems. For the stochastic sub-map, we employ a generative model, particularly generative adversarial networks (GANs) in this paper. The final constructed stochastic flow map then defines a stochastic evolution model that is a weak approximation, in term of distribution, of the unknown stochastic system. A comprehensive set of numerical examples are presented to demonstrate the flexibility and effectiveness of the proposed sFML method for various types of stochastic systems.

2305.05532

2023-05-05T01:23:56Z

An ensemble of convolution-based methods for fault detection using vibration signals

This paper focuses on solving a fault detection problem using multivariate time series of vibration signals collected from planetary gearboxes in a test rig. Various traditional machine learning and deep learning methods have been proposed for multivariate time-series classification, including distance-based, functional data-oriented, feature-driven, and convolution kernel-based methods. Recent studies have shown using convolution kernel-based methods like ROCKET, and 1D convolutional neural networks with ResNet and FCN, have robust performance for multivariate time-series data classification. We propose an ensemble of three convolution kernel-based methods and show its efficacy on this fault detection problem by outperforming other approaches and achieving an accuracy of more than 98.8\%.

2305.05543

2023-05-05T10:47:34Z

Walk4Me: Telehealth Community Mobility Assessment, An Automated System for Early Diagnosis and Disease Progression

We introduce Walk4Me, a telehealth community mobility assessment system designed to facilitate early diagnosis, severity, and progression identification. Our system achieves this by 1) enabling early diagnosis, 2) identifying early indicators of clinical severity, and 3) quantifying and tracking the progression of the disease across the ambulatory phase of the disease. To accomplish this, we employ an Artificial Intelligence (AI)-based detection of gait characteristics in patients and typically developing peers. Our system remotely and in real-time collects data from device sensors (e.g., acceleration from a mobile device, etc.) using our novel Walk4Me API. Our web application extracts temporal/spatial gait characteristics and raw data signal characteristics and then employs traditional machine learning and deep learning techniques to identify patterns that can 1) identify patients with gait disturbances associated with disease, 2) describe the degree of mobility limitation, and 3) identify characteristics that change over time with disease progression. We have identified several machine learning techniques that differentiate between patients and typically-developing subjects with 100% accuracy across the age range studied, and we have also identified corresponding temporal/spatial gait characteristics associated with each group. Our work demonstrates the potential of utilizing the latest advances in mobile device and machine learning technology to measure clinical outcomes regardless of the point of care, inform early clinical diagnosis and treatment decision-making, and monitor disease progression.

2305.03936

2023-05-06T05:34:03Z

Annotation-efficient learning for OCT segmentation

Deep learning has been successfully applied to OCT segmentation. However, for data from different manufacturers and imaging protocols, and for different regions of interest (ROIs), it requires laborious and time-consuming data annotation and training, which is undesirable in many scenarios, such as surgical navigation and multi-center clinical trials. Here we propose an annotation-efficient learning method for OCT segmentation that could significantly reduce annotation costs. Leveraging self-supervised generative learning, we train a Transformer-based model to learn the OCT imagery. Then we connect the trained Transformer-based encoder to a CNN-based decoder, to learn the dense pixel-wise prediction in OCT segmentation. These training phases use open-access data and thus incur no annotation costs, and the pre-trained model can be adapted to different data and ROIs without re-training. Based on the greedy approximation for the k-center problem, we also introduce an algorithm for the selective annotation of the target data. We verified our method on publicly-available and private OCT datasets. Compared to the widely-used U-Net model with 100% training data, our method only requires ~10% of the data for achieving the same segmentation accuracy, and it speeds the training up to ~3.5 times. Furthermore, our proposed method outperforms other potential strategies that could improve annotation efficiency. We think this emphasis on learning efficiency may help improve the intelligence and application penetration of OCT-based technologies. Our code and pre-trained model are publicly available at https://github.com/SJTU-Intelligent-Optics-Lab/Annotation-efficient-learning-for-OCT-segmentation.

2305.03966

2023-05-06T07:57:38Z

Feature Chirality in Deep Learning Models

As deep learning applications extensively increase by leaps and bounds, their interpretability has become increasingly prominent. As a universal property, chirality exists widely in nature, and applying it to the explanatory research of deep learning may be helpful to some extent. Inspired by a recent study that used CNN (convolutional neural network), which applied visual chirality, to distinguish whether an image is flipped or not. In this paper, we study feature chirality innovatively, which shows how the statistics of deep learning models' feature data are changed by training. We rethink the feature-level chirality property, propose the feature chirality, and give the measure. Our analysis of feature chirality on AlexNet, VGG, and ResNet reveals similar but surprising results, including the prevalence of feature chirality in these models, the initialization methods of the models do not affect feature chirality. Our work shows that feature chirality implies model evaluation, interpretability of the model, and model parameters optimization.

2305.04007

2023-05-06T10:46:56Z

Weighted Point Cloud Normal Estimation

Existing normal estimation methods for point clouds are often less robust to severe noise and complex geometric structures. Also, they usually ignore the contributions of different neighbouring points during normal estimation, which leads to less accurate results. In this paper, we introduce a weighted normal estimation method for 3D point cloud data. We innovate in two key points: 1) we develop a novel weighted normal regression technique that predicts point-wise weights from local point patches and use them for robust, feature-preserving normal regression; 2) we propose to conduct contrastive learning between point patches and the corresponding ground-truth normals of the patches' central points as a pre-training process to facilitate normal regression. Comprehensive experiments demonstrate that our method can robustly handle noisy and complex point clouds, achieving state-of-the-art performance on both synthetic and real-world datasets.

2305.04075

2023-05-06T15:47:48Z

PointCMP: Contrastive Mask Prediction for Self-supervised Learning on Point Cloud Videos

Self-supervised learning can extract representations of good quality from solely unlabeled data, which is appealing for point cloud videos due to their high labelling cost. In this paper, we propose a contrastive mask prediction (PointCMP) framework for self-supervised learning on point cloud videos. Specifically, our PointCMP employs a two-branch structure to achieve simultaneous learning of both local and global spatio-temporal information. On top of this two-branch structure, a mutual similarity based augmentation module is developed to synthesize hard samples at the feature level. By masking dominant tokens and erasing principal channels, we generate hard samples to facilitate learning representations with better discrimination and generalization performance. Extensive experiments show that our PointCMP achieves the state-of-the-art performance on benchmark datasets and outperforms existing full-supervised counterparts. Transfer learning results demonstrate the superiority of the learned representations across different datasets and tasks.

2305.04123

2023-05-06T19:29:28Z

Transform-Equivariant Consistency Learning for Temporal Sentence Grounding

This paper addresses the temporal sentence grounding (TSG). Although existing methods have made decent achievements in this task, they not only severely rely on abundant video-query paired data for training, but also easily fail into the dataset distribution bias. To alleviate these limitations, we introduce a novel Equivariant Consistency Regulation Learning (ECRL) framework to learn more discriminative query-related frame-wise representations for each video, in a self-supervised manner. Our motivation comes from that the temporal boundary of the query-guided activity should be consistently predicted under various video-level transformations. Concretely, we first design a series of spatio-temporal augmentations on both foreground and background video segments to generate a set of synthetic video samples. In particular, we devise a self-refine module to enhance the completeness and smoothness of the augmented video. Then, we present a novel self-supervised consistency loss (SSCL) applied on the original and augmented videos to capture their invariant query-related semantic by minimizing the KL-divergence between the sequence similarity of two videos and a prior Gaussian distribution of timestamp distance. At last, a shared grounding head is introduced to predict the transform-equivariant query-guided segment boundaries for both the original and augmented videos. Extensive experiments on three challenging datasets (ActivityNet, TACoS, and Charades-STA) demonstrate both effectiveness and efficiency of our proposed ECRL framework.

2305.03912

2023-05-06T03:31:56Z

White Matter Hyperintensities Segmentation Using Probabilistic TransUNet

White Matter Hyperintensities (WMH) are areas of the brain that have higher intensity than other normal brain regions on Magnetic Resonance Imaging (MRI) scans. WMH is often associated with small vessel disease in the brain, making early detection of WMH important. However, there are two common issues in the detection of WMH: high ambiguity and difficulty in detecting small WMH. In this study, we propose a method called Probabilistic TransUNet to address the precision of small object segmentation and the high ambiguity of medical images. To measure model performance, we conducted a k-fold cross validation and cross dataset robustness experiment. Based on the experiments, the addition of a probabilistic model and the use of a transformer-based approach were able to achieve better performance.

2305.03980

2023-05-06T09:00:50Z

Towards Prompt-robust Face Privacy Protection via Adversarial Decoupling Augmentation Framework

Denoising diffusion models have shown remarkable potential in various generation tasks. The open-source large-scale text-to-image model, Stable Diffusion, becomes prevalent as it can generate realistic artistic or facial images with personalization through fine-tuning on a limited number of new samples. However, this has raised privacy concerns as adversaries can acquire facial images online and fine-tune text-to-image models for malicious editing, leading to baseless scandals, defamation, and disruption to victims' lives. Prior research efforts have focused on deriving adversarial loss from conventional training processes for facial privacy protection through adversarial perturbations. However, existing algorithms face two issues: 1) they neglect the image-text fusion module, which is the vital module of text-to-image diffusion models, and 2) their defensive performance is unstable against different attacker prompts. In this paper, we propose the Adversarial Decoupling Augmentation Framework (ADAF), addressing these issues by targeting the image-text fusion module to enhance the defensive performance of facial privacy protection algorithms. ADAF introduces multi-level text-related augmentations for defense stability against various attacker prompts. Concretely, considering the vision, text, and common unit space, we propose Vision-Adversarial Loss, Prompt-Robust Augmentation, and Attention-Decoupling Loss. Extensive experiments on CelebA-HQ and VGGFace2 demonstrate ADAF's promising performance, surpassing existing algorithms.

2305.04047

2023-05-06T13:28:20Z

Degradation-Noise-Aware Deep Unfolding Transformer for Hyperspectral Image Denoising

Hyperspectral imaging (HI) has emerged as a powerful tool in diverse fields such as medical diagnosis, industrial inspection, and agriculture, owing to its ability to detect subtle differences in physical properties through high spectral resolution. However, hyperspectral images (HSIs) are often quite noisy because of narrow band spectral filtering. To reduce the noise in HSI data cubes, both model-driven and learning-based denoising algorithms have been proposed. However, model-based approaches rely on hand-crafted priors and hyperparameters, while learning-based methods are incapable of estimating the inherent degradation patterns and noise distributions in the imaging procedure, which could inform supervised learning. Secondly, learning-based algorithms predominantly rely on CNN and fail to capture long-range dependencies, resulting in limited interpretability. This paper proposes a Degradation-Noise-Aware Unfolding Network (DNA-Net) that addresses these issues. Firstly, DNA-Net models sparse noise, Gaussian noise, and explicitly represent image prior using transformer. Then the model is unfolded into an end-to-end network, the hyperparameters within the model are estimated from the noisy HSI and degradation model and utilizes them to control each iteration. Additionally, we introduce a novel U-Shaped Local-Non-local-Spectral Transformer (U-LNSA) that captures spectral correlation, local contents, and non-local dependencies simultaneously. By integrating U-LNSA into DNA-Net, we present the first Transformer-based deep unfolding HSI denoising method. Experimental results show that DNA-Net outperforms state-of-the-art methods, and the modeling of noise distributions helps in cases with heavy noise.

2305.04054

2023-05-06T14:01:02Z

SST-ReversibleNet: Reversible-prior-based Spectral-Spatial Transformer for Efficient Hyperspectral Image Reconstruction

Spectral image reconstruction is an important task in snapshot compressed imaging. This paper aims to propose a new end-to-end framework with iterative capabilities similar to a deep unfolding network to improve reconstruction accuracy, independent of optimization conditions, and to reduce the number of parameters. A novel framework called the reversible-prior-based method is proposed. Inspired by the reversibility of the optical path, the reversible-prior-based framework projects the reconstructions back into the measurement space, and then the residuals between the projected data and the real measurements are fed into the network for iteration. The reconstruction subnet in the network then learns the mapping of the residuals to the true values to improve reconstruction accuracy. In addition, a novel spectral-spatial transformer is proposed to account for the global correlation of spectral data in both spatial and spectral dimensions while balancing network depth and computational complexity, in response to the shortcomings of existing transformer-based denoising modules that ignore spatial texture features or learn local spatial features at the expense of global spatial features. Extensive experiments show that our SST-ReversibleNet significantly outperforms state-of-the-art methods on simulated and real HSI datasets, while requiring lower computational and storage costs. https://github.com/caizeyu1992/SST

2305.03899

2023-05-06T02:34:28Z

NL-CS Net: Deep Learning with Non-Local Prior for Image Compressive Sensing

Deep learning has been applied to compressive sensing (CS) of images successfully in recent years. However, existing network-based methods are often trained as the black box, in which the lack of prior knowledge is often the bottleneck for further performance improvement. To overcome this drawback, this paper proposes a novel CS method using non-local prior which combines the interpretability of the traditional optimization methods with the speed of network-based methods, called NL-CS Net. We unroll each phase from iteration of the augmented Lagrangian method solving non-local and sparse regularized optimization problem by a network. NL-CS Net is composed of the up-sampling module and the recovery module. In the up-sampling module, we use learnable up-sampling matrix instead of a predefined one. In the recovery module, patch-wise non-local network is employed to capture long-range feature correspondences. Important parameters involved (e.g. sampling matrix, nonlinear transforms, shrinkage thresholds, step size, $etc.$) are learned end-to-end, rather than hand-crafted. Furthermore, to facilitate practical implementation, orthogonal and binary constraints on the sampling matrix are simultaneously adopted. Extensive experiments on natural images and magnetic resonance imaging (MRI) demonstrate that the proposed method outperforms the state-of-the-art methods while maintaining great interpretability and speed.

2305.03915

2023-05-06T03:39:00Z

HateMM: A Multi-Modal Dataset for Hate Video Classification

Hate speech has become one of the most significant issues in modern society, having implications in both the online and the offline world. Due to this, hate speech research has recently gained a lot of traction. However, most of the work has primarily focused on text media with relatively little work on images and even lesser on videos. Thus, early stage automated video moderation techniques are needed to handle the videos that are being uploaded to keep the platform safe and healthy. With a view to detect and remove hateful content from the video sharing platforms, our work focuses on hate video detection using multi-modalities. To this end, we curate ~43 hours of videos from BitChute and manually annotate them as hate or non-hate, along with the frame spans which could explain the labelling decision. To collect the relevant videos we harnessed search keywords from hate lexicons. We observe various cues in images and audio of hateful videos. Further, we build deep learning multi-modal models to classify the hate videos and observe that using all the modalities of the videos improves the overall hate speech detection performance (accuracy=0.798, macro F1-score=0.790) by ~5.7% compared to the best uni-modal model in terms of macro F1 score. In summary, our work takes the first step toward understanding and modeling hateful videos on video hosting platforms such as BitChute.

2305.04021

2023-05-06T11:45:45Z

A Sea-Land Clutter Classification Framework for Over-the-Horizon-Radar Based on Weighted Loss Semi-supervised GAN

Deep convolutional neural network has made great achievements in sea-land clutter classification for over-the-horizon-radar (OTHR). The premise is that a large number of labeled training samples must be provided for a sea-land clutter classifier. In practical engineering applications, it is relatively easy to obtain label-free sea-land clutter samples. However, the labeling process is extremely cumbersome and requires expertise in the field of OTHR. To solve this problem, we propose an improved generative adversarial network, namely weighted loss semi-supervised generative adversarial network (WL-SSGAN). Specifically, we propose a joint feature matching loss by weighting the middle layer features of the discriminator of semi-supervised generative adversarial network. Furthermore, we propose the weighted loss of WL-SSGAN by linearly weighting standard adversarial loss and joint feature matching loss. The semi-supervised classification performance of WL-SSGAN is evaluated on a sea-land clutter dataset. The experimental results show that WL-SSGAN can improve the performance of the fully supervised classifier with only a small number of labeled samples by utilizing a large number of unlabeled sea-land clutter samples. Further, the proposed weighted loss is superior to both the adversarial loss and the feature matching loss. Additionally, we compare WL-SSGAN with conventional semi-supervised classification methods and demonstrate that WL-SSGAN achieves the highest classification accuracy.

2305.04095

2023-05-06T16:47:52Z

Gradient Leakage Defense with Key-Lock Module for Federated Learning

Federated Learning (FL) is a widely adopted privacy-preserving machine learning approach where private data remains local, enabling secure computations and the exchange of local model gradients between local clients and third-party parameter servers. However, recent findings reveal that privacy may be compromised and sensitive information potentially recovered from shared gradients. In this study, we offer detailed analysis and a novel perspective on understanding the gradient leakage problem. These theoretical works lead to a new gradient leakage defense technique that secures arbitrary model architectures using a private key-lock module. Only the locked gradient is transmitted to the parameter server for global model aggregation. Our proposed learning method is resistant to gradient leakage attacks, and the key-lock module is designed and trained to ensure that, without the private information of the key-lock module: a) reconstructing private training data from the shared gradient is infeasible; and b) the global model's inference performance is significantly compromised. We discuss the theoretical underpinnings of why gradients can leak private information and provide theoretical proof of our method's effectiveness. We conducted extensive empirical evaluations with a total of forty-four models on several popular benchmarks, demonstrating the robustness of our proposed approach in both maintaining model performance and defending against gradient leakage attacks.

2305.04142

2023-05-06T22:14:13Z

Transformer-Based Hierarchical Clustering for Brain Network Analysis

Brain networks, graphical models such as those constructed from MRI, have been widely used in pathological prediction and analysis of brain functions. Within the complex brain system, differences in neuronal connection strengths parcellate the brain into various functional modules (network communities), which are critical for brain analysis. However, identifying such communities within the brain has been a nontrivial issue due to the complexity of neuronal interactions. In this work, we propose a novel interpretable transformer-based model for joint hierarchical cluster identification and brain network classification. Extensive experimental results on real-world brain network datasets show that with the help of hierarchical clustering, the model achieves increased accuracy and reduced runtime complexity while providing plausible insight into the functional organization of brain regions. The implementation is available at https://github.com/DDVD233/THC.

2305.03880

2023-05-06T00:20:24Z

NorBench -- A Benchmark for Norwegian Language Models

We present NorBench: a streamlined suite of NLP tasks and probes for evaluating Norwegian language models (LMs) on standardized data splits and evaluation metrics. We also introduce a range of new Norwegian language models (both encoder and encoder-decoder based). Finally, we compare and analyze their performance, along with other existing LMs, across the different benchmark tests of NorBench.

2305.03949

2023-05-06T06:30:29Z

Label-Free Multi-Domain Machine Translation with Stage-wise Training

Most multi-domain machine translation models rely on domain-annotated data. Unfortunately, domain labels are usually unavailable in both training processes and real translation scenarios. In this work, we propose a label-free multi-domain machine translation model which requires only a few or no domain-annotated data in training and no domain labels in inference. Our model is composed of three parts: a backbone model, a domain discriminator taking responsibility to discriminate data from different domains, and a set of experts that transfer the decoded features from generic to specific. We design a stage-wise training strategy and train the three parts sequentially. To leverage the extra domain knowledge and improve the training stability, in the discriminator training stage, domain differences are modeled explicitly with clustering and distilled into the discriminator through a multi-classification task. Meanwhile, the Gumbel-Max sampling is adopted as the routing scheme in the expert training stage to achieve the balance of each expert in specialization and generalization. Experimental results on the German-to-English translation task show that our model significantly improves BLEU scores on six different domains and even outperforms most of the models trained with domain-annotated data.

2305.03970

2023-05-06T08:05:22Z

NER-to-MRC: Named-Entity Recognition Completely Solving as Machine Reading Comprehension

Named-entity recognition (NER) detects texts with predefined semantic labels and is an essential building block for natural language processing (NLP). Notably, recent NER research focuses on utilizing massive extra data, including pre-training corpora and incorporating search engines. However, these methods suffer from high costs associated with data collection and pre-training, and additional training process of the retrieved data from search engines. To address the above challenges, we completely frame NER as a machine reading comprehension (MRC) problem, called NER-to-MRC, by leveraging MRC with its ability to exploit existing data efficiently. Several prior works have been dedicated to employing MRC-based solutions for tackling the NER problem, several challenges persist: i) the reliance on manually designed prompts; ii) the limited MRC approaches to data reconstruction, which fails to achieve performance on par with methods utilizing extensive additional data. Thus, our NER-to-MRC conversion consists of two components: i) transform the NER task into a form suitable for the model to solve with MRC in a efficient manner; ii) apply the MRC reasoning strategy to the model. We experiment on 6 benchmark datasets from three domains and achieve state-of-the-art performance without external data, up to 11.24% improvement on the WNUT-16 dataset.

2305.03973

2023-05-06T08:16:07Z

DiscoPrompt: Path Prediction Prompt Tuning for Implicit Discourse Relation Recognition

Implicit Discourse Relation Recognition (IDRR) is a sophisticated and challenging task to recognize the discourse relations between the arguments with the absence of discourse connectives. The sense labels for each discourse relation follow a hierarchical classification scheme in the annotation process (Prasad et al., 2008), forming a hierarchy structure. Most existing works do not well incorporate the hierarchy structure but focus on the syntax features and the prior knowledge of connectives in the manner of pure text classification. We argue that it is more effective to predict the paths inside the hierarchical tree (e.g., "Comparison -> Contrast -> however") rather than flat labels (e.g., Contrast) or connectives (e.g., however). We propose a prompt-based path prediction method to utilize the interactive information and intrinsic senses among the hierarchy in IDRR. This is the first work that injects such structure information into pre-trained language models via prompt tuning, and the performance of our solution shows significant and consistent improvement against competitive baselines.

2305.03981

2023-05-06T09:02:10Z

Pre-training Language Model as a Multi-perspective Course Learner

ELECTRA, the generator-discriminator pre-training framework, has achieved impressive semantic construction capability among various downstream tasks. Despite the convincing performance, ELECTRA still faces the challenges of monotonous training and deficient interaction. Generator with only masked language modeling (MLM) leads to biased learning and label imbalance for discriminator, decreasing learning efficiency; no explicit feedback loop from discriminator to generator results in the chasm between these two components, underutilizing the course learning. In this study, a multi-perspective course learning (MCL) method is proposed to fetch a many degrees and visual angles for sample-efficient pre-training, and to fully leverage the relationship between generator and discriminator. Concretely, three self-supervision courses are designed to alleviate inherent flaws of MLM and balance the label in a multi-perspective way. Besides, two self-correction courses are proposed to bridge the chasm between the two encoders by creating a "correction notebook" for secondary-supervision. Moreover, a course soups trial is conducted to solve the "tug-of-war" dynamics problem of MCL, evolving a stronger pre-trained model. Experimental results show that our method significantly improves ELECTRA's average performance by 2.8% and 3.2% absolute points respectively on GLUE and SQuAD 2.0 benchmarks, and overshadows recent advanced ELECTRA-style models under the same settings. The pre-trained MCL model is available at https://huggingface.co/McmanusChen/MCL-base.

2305.04044

2023-05-06T13:20:31Z

Diffusion-NAT: Self-Prompting Discrete Diffusion for Non-Autoregressive Text Generation

Recently, continuous diffusion models (CDM) have been introduced into non-autoregressive (NAR) text-to-text generation. However, the discrete nature of text increases the difficulty of CDM to generate coherent and fluent texts, and also causes the incompatibility problem between CDM and advanced NLP techniques, especially the popular pre-trained language models~(PLMs). To solve it, we propose Diffusion-NAT, which introduces discrete diffusion models~(DDM) into NAR text-to-text generation and integrates BART to improve the performance. By revising the decoding process of BART and the typical settings of DDM, we unify the inference process of BART and the denoising process of DDM into the same NAR masked tokens recovering task. In this way, DDM can rely on BART to perform denoising, which can benefit from both the rich pre-learned knowledge of BART and the iterative refining paradigm of DDM. Besides, we also propose the iterative self-prompting strategy to further improve the generation quality. Experimental results on 7 datasets show that our approach can outperform competitive NAR methods, and even surpass autoregressive methods. Our code and data will be publicly released.

2305.04049

2023-05-06T13:33:33Z

Actively Discovering New Slots for Task-oriented Conversation

Existing task-oriented conversational search systems heavily rely on domain ontologies with pre-defined slots and candidate value sets. In practical applications, these prerequisites are hard to meet, due to the emerging new user requirements and ever-changing scenarios. To mitigate these issues for better interaction performance, there are efforts working towards detecting out-of-vocabulary values or discovering new slots under unsupervised or semi-supervised learning paradigm. However, overemphasizing on the conversation data patterns alone induces these methods to yield noisy and arbitrary slot results. To facilitate the pragmatic utility, real-world systems tend to provide a stringent amount of human labelling quota, which offers an authoritative way to obtain accurate and meaningful slot assignments. Nonetheless, it also brings forward the high requirement of utilizing such quota efficiently. Hence, we formulate a general new slot discovery task in an information extraction fashion and incorporate it into an active learning framework to realize human-in-the-loop learning. Specifically, we leverage existing language tools to extract value candidates where the corresponding labels are further leveraged as weak supervision signals. Based on these, we propose a bi-criteria selection scheme which incorporates two major strategies, namely, uncertainty-based sampling and diversity-based sampling to efficiently identify terms of interest. We conduct extensive experiments on several public datasets and compare with a bunch of competitive baselines to demonstrate the effectiveness of our method. We have made the code and data used in this paper publicly available.

2305.04100

2023-05-06T17:04:51Z

Rhetorical Role Labeling of Legal Documents using Transformers and Graph Neural Networks

A legal document is usually long and dense requiring human effort to parse it. It also contains significant amounts of jargon which make deriving insights from it using existing models a poor approach. This paper presents the approaches undertaken to perform the task of rhetorical role labelling on Indian Court Judgements as part of SemEval Task 6: understanding legal texts, shared subtask A. We experiment with graph based approaches like Graph Convolutional Networks and Label Propagation Algorithm, and transformer-based approaches including variants of BERT to improve accuracy scores on text classification of complex legal documents.

2305.03937

2023-05-06T05:35:14Z

Residual Prompt Tuning: Improving Prompt Tuning with Residual Reparameterization

Prompt tuning is one of the successful approaches for parameter-efficient tuning of pre-trained language models. Despite being arguably the most parameter-efficient (tuned soft prompts constitute <0.1% of total parameters), it typically performs worse than other efficient tuning methods and is quite sensitive to hyper-parameters. In this work, we introduce Residual Prompt Tuning - a simple and efficient method that significantly improves the performance and stability of prompt tuning. We propose to reparameterize soft prompt embeddings using a shallow network with a residual connection. Our experiments show that Residual Prompt Tuning significantly outperforms prompt tuning on SuperGLUE benchmark. Notably, our method reaches +7 points improvement over prompt tuning with T5-Base and allows to reduce the prompt length by 10x without hurting performance. In addition, we show that our approach is robust to the choice of learning rate and prompt initialization, and is effective in few-shot settings.

2305.03987

2023-05-06T09:27:58Z

Replicating Complex Dialogue Policy of Humans via Offline Imitation Learning with Supervised Regularization

Policy learning (PL) is a module of a task-oriented dialogue system that trains an agent to make actions in each dialogue turn. Imitating human action is a fundamental problem of PL. However, both supervised learning (SL) and reinforcement learning (RL) frameworks cannot imitate humans well. Training RL models require online interactions with user simulators, while simulating complex human policy is hard. Performances of SL-based models are restricted because of the covariate shift problem. Specifically, a dialogue is a sequential decision-making process where slight differences in current utterances and actions will cause significant differences in subsequent utterances. Therefore, the generalize ability of SL models is restricted because statistical characteristics of training and testing dialogue data gradually become different. This study proposed an offline imitation learning model that learns policy from real dialogue datasets and does not require user simulators. It also utilizes state transition information, which alleviates the influence of the covariate shift problem. We introduced a regularization trick to make our model can be effectively optimized. We investigated the performance of our model on four independent public dialogue datasets. The experimental result showed that our model performed better in the action prediction task.

2305.04039

2023-05-06T13:03:45Z

Refining the Responses of LLMs by Themselves

In this paper, we propose a simple yet efficient approach based on prompt engineering that leverages the large language model itself to optimize its answers without relying on auxiliary models. We introduce an iterative self-evaluating optimization mechanism, with the potential for improved output quality as iterations progress, removing the need for manual intervention. The experiment's findings indicate that utilizing our response refinement framework on the GPT-3.5 model yields results that are on par with, or even surpass, those generated by the cutting-edge GPT-4 model. Detailed implementation strategies and illustrative examples are provided to demonstrate the superiority of our proposed solution.

2305.04147

2023-05-06T23:11:25Z

Controllable Mixed-Initiative Dialogue Generation through Prompting

Mixed-initiative dialogue tasks involve repeated exchanges of information and conversational control. Conversational agents gain control by generating responses that follow particular dialogue intents or strategies, prescribed by a policy planner. The standard approach has been fine-tuning pre-trained language models to perform generation conditioned on these intents. However, these supervised generation models are limited by the cost and quality of data annotation. We instead prompt large language models as a drop-in replacement to fine-tuning on conditional generation. We formalize prompt construction for controllable mixed-initiative dialogue. Our findings show improvements over fine-tuning and ground truth responses according to human evaluation and automatic metrics for two tasks: PersuasionForGood and Emotional Support Conversations.

2305.03883

2023-05-06T00:38:29Z

SINCERE: Sequential Interaction Networks representation learning on Co-Evolving RiEmannian manifolds

Sequential interaction networks (SIN) have been commonly adopted in many applications such as recommendation systems, search engines and social networks to describe the mutual influence between users and items/products. Efforts on representing SIN are mainly focused on capturing the dynamics of networks in Euclidean space, and recently plenty of work has extended to hyperbolic geometry for implicit hierarchical learning. Previous approaches which learn the embedding trajectories of users and items achieve promising results. However, there are still a range of fundamental issues remaining open. For example, is it appropriate to place user and item nodes in one identical space regardless of their inherent discrepancy? Instead of residing in a single fixed curvature space, how will the representation spaces evolve when new interaction occurs? To explore these issues for sequential interaction networks, we propose SINCERE, a novel method representing Sequential Interaction Networks on Co-Evolving RiEmannian manifolds. SIN- CERE not only takes the user and item embedding trajectories in respective spaces into account, but also emphasizes on the space evolvement that how curvature changes over time. Specifically, we introduce a fresh cross-geometry aggregation which allows us to propagate information across different Riemannian manifolds without breaking conformal invariance, and a curvature estimator which is delicately designed to predict global curvatures effectively according to current local Ricci curvatures. Extensive experiments on several real-world datasets demonstrate the promising performance of SINCERE over the state-of-the-art sequential interaction prediction methods.

2305.03901

2023-05-06T02:41:03Z

Synthesizing PET images from High-field and Ultra-high-field MR images Using Joint Diffusion Attention Model

MRI and PET are crucial diagnostic tools for brain diseases, as they provide complementary information on brain structure and function. However, PET scanning is costly and involves radioactive exposure, resulting in a lack of PET. Moreover, simultaneous PET and MRI at ultra-high-field are currently hardly infeasible. Ultra-high-field imaging has unquestionably proven valuable in both clinical and academic settings, especially in the field of cognitive neuroimaging. These motivate us to propose a method for synthetic PET from high-filed MRI and ultra-high-field MRI. From a statistical perspective, the joint probability distribution (JPD) is the most direct and fundamental means of portraying the correlation between PET and MRI. This paper proposes a novel joint diffusion attention model which has the joint probability distribution and attention strategy, named JDAM. JDAM has a diffusion process and a sampling process. The diffusion process involves the gradual diffusion of PET to Gaussian noise by adding Gaussian noise, while MRI remains fixed. JPD of MRI and noise-added PET was learned in the diffusion process. The sampling process is a predictor-corrector. PET images were generated from MRI by JPD of MRI and noise-added PET. The predictor is a reverse diffusion process and the corrector is Langevin dynamics. Experimental results on the public Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset demonstrate that the proposed method outperforms state-of-the-art CycleGAN for high-field MRI (3T MRI). Finally, synthetic PET images from the ultra-high-field (5T MRI and 7T MRI) be attempted, providing a possibility for ultra-high-field PET-MRI imaging.

2305.03934

2023-05-06T05:20:39Z

Revisiting Lightweight Compiler Provenance Recovery on ARM Binaries

A binary's behavior is greatly influenced by how the compiler builds its source code. Although most compiler configuration details are abstracted away during compilation, recovering them is useful for reverse engineering and program comprehension tasks on unknown binaries, such as code similarity detection. We observe that previous work has thoroughly explored this on x86-64 binaries. However, there has been limited investigation of ARM binaries, which are increasingly prevalent. In this paper, we extend previous work with a shallow-learning model that efficiently and accurately recovers compiler configuration properties for ARM binaries. We apply opcode and register-derived features, that have previously been effective on x86-64 binaries, to ARM binaries. Furthermore, we compare this work with Pizzolotto et al., a recent architecture-agnostic model that uses deep learning, whose dataset and code are available. We observe that the lightweight features are reproducible on ARM binaries. We achieve over 99% accuracy, on par with state-of-the-art deep learning approaches, while achieving a 583-times speedup during training and 3,826-times speedup during inference. Finally, we also discuss findings of overfitting that was previously undetected in prior work.

2305.04006

2023-05-06T10:44:38Z

Electromyography Signal Classification Using Deep Learning

We have implemented a deep learning model with L2 regularization and trained it on Electromyography (EMG) data. The data comprises of EMG signals collected from control group, myopathy and ALS patients. Our proposed deep neural network consists of eight layers; five fully connected, two batch normalization and one dropout layers. The data is divided into training and testing sections by subsequently dividing the training data into sub-training and validation sections. Having implemented this model, an accuracy of 99 percent is achieved on the test data set. The model was able to distinguishes the normal cases (control group) from the others at a precision of 100 percent and classify the myopathy and ALS with high accuracy of 97.4 and 98.2 percents, respectively. Thus we believe that, this highly improved classification accuracies will be beneficial for their use in the clinical diagnosis of neuromuscular disorders.

2305.04093

2023-05-06T16:42:11Z

An improved regret analysis for UCB-N and TS-N

In the setting of stochastic online learning with undirected feedback graphs, Lykouris et al. (2020) previously analyzed the pseudo-regret of the upper confidence bound-based algorithm UCB-N and the Thompson Sampling-based algorithm TS-N. In this note, we show how to improve their pseudo-regret analysis. Our improvement involves refining a key lemma of the previous analysis, allowing a $\log(T)$ factor to be replaced by a factor $\log_2(\alpha) + 3$ for $\alpha$ the independence number of the feedback graph.

2305.04135

2023-05-06T20:56:20Z

Maintaining Stability and Plasticity for Predictive Churn Reduction

Deployed machine learning models should be updated to take advantage of a larger sample size to improve performance, as more data is gathered over time. Unfortunately, even when model updates improve aggregate metrics such as accuracy, they can lead to errors on samples that were correctly predicted by the previous model causing per-sample regression in performance known as predictive churn. Such prediction flips erode user trust thereby reducing the effectiveness of the human-AI team as a whole. We propose a solution called Accumulated Model Combination (AMC) based keeping the previous and current model version, and generating a meta-output using the prediction of the two models. AMC is a general technique and we propose several instances of it, each having their own advantages depending on the model and data properties. AMC requires minimal additional computation and changes to training procedures. We motivate the need for AMC by showing the difficulty of making a single model consistent with its own predictions throughout training thereby revealing an implicit stability-plasticity tradeoff when training a single model. We demonstrate the effectiveness of AMC on a variety of modalities including computer vision, text, and tabular datasets comparing against state-of-the-art churn reduction methods, and showing superior churn reduction ability compared to all existing methods while being more efficient than ensembles.

2305.03894

2023-05-06T02:08:19Z

Twin support vector quantile regression

We propose a twin support vector quantile regression (TSVQR) to capture the heterogeneous and asymmetric information in modern data. Using a quantile parameter, TSVQR effectively depicts the heterogeneous distribution information with respect to all portions of data points. Correspondingly, TSVQR constructs two smaller sized quadratic programming problems (QPPs) to generate two nonparallel planes to measure the distributional asymmetry between the lower and upper bounds at each quantile level. The QPPs in TSVQR are smaller and easier to solve than those in previous quantile regression methods. Moreover, the dual coordinate descent algorithm for TSVQR also accelerates the training speed. Experimental results on six artiffcial data sets, ffve benchmark data sets, two large scale data sets, two time-series data sets, and two imbalanced data sets indicate that the TSVQR outperforms previous quantile regression methods in terms of the effectiveness of completely capturing the heterogeneous and asymmetric information and the efffciency of the learning process.

2305.03900

2023-05-06T02:36:39Z

Rethinking Class Imbalance in Machine Learning

Imbalance learning is a subfield of machine learning that focuses on learning tasks in the presence of class imbalance. Nearly all existing studies refer to class imbalance as a proportion imbalance, where the proportion of training samples in each class is not balanced. The ignorance of the proportion imbalance will result in unfairness between/among classes and poor generalization capability. Previous literature has presented numerous methods for either theoretical/empirical analysis or new methods for imbalance learning. This study presents a new taxonomy of class imbalance in machine learning with a broader scope. Four other types of imbalance, namely, variance, distance, neighborhood, and quality imbalances between/among classes, which may exist in machine learning tasks, are summarized. Two different levels of imbalance including global and local are also presented. Theoretical analysis is used to illustrate the significant impact of the new imbalance types on learning fairness. Moreover, our taxonomy and theoretical conclusions are used to analyze the shortcomings of several classical methods. As an example, we propose a new logit perturbation-based imbalance learning loss when proportion, variance, and distance imbalances exist simultaneously. Several classical losses become the special case of our proposed method. Meta learning is utilized to infer the hyper-parameters related to the three types of imbalance. Experimental results on several benchmark corpora validate the effectiveness of the proposed method.

2305.03956

2023-05-06T06:45:47Z

Machine-Learning-Based Classification of GPS Signal Reception Conditions Using a Dual-Polarized Antenna in Urban Areas

In urban areas, dense buildings frequently block and reflect global positioning system (GPS) signals, resulting in the reception of a few visible satellites with many multipath signals. This is a significant problem that results in unreliable positioning in urban areas. If a signal reception condition from a certain satellite can be detected, the positioning performance can be improved by excluding or de-weighting the multipath contaminated satellite signal. Thus, we developed a machine-learning-based method of classifying GPS signal reception conditions using a dual-polarized antenna. We employed a decision tree algorithm for classification using three features, one of which can be obtained only from a dual-polarized antenna. A machine-learning model was trained using GPS signals collected from various locations. When the features extracted from the GPS raw signal are input, the generated machine-learning model outputs one of the three signal reception conditions: non-line-of-sight (NLOS) only, line-of-sight (LOS) only, or LOS+NLOS. Multiple testing datasets were used to analyze the classification accuracy, which was then compared with an existing method using dual single-polarized antennas. Consequently, when the testing dataset was collected at different locations from the training dataset, a classification accuracy of 64.47% was obtained, which was slightly higher than the accuracy of the existing method using dual single-polarized antennas. Therefore, the dual-polarized antenna solution is more beneficial than the dual single-polarized antenna solution because it has a more compact form factor and its performance is similar to that of the other solution.

2305.04146

2023-05-06T23:00:41Z

Bounding the Invertibility of Privacy-preserving Instance Encoding using Fisher Information

Privacy-preserving instance encoding aims to encode raw data as feature vectors without revealing their privacy-sensitive information. When designed properly, these encodings can be used for downstream ML applications such as training and inference with limited privacy risk. However, the vast majority of existing instance encoding schemes are based on heuristics and their privacy-preserving properties are only validated empirically against a limited set of attacks. In this paper, we propose a theoretically-principled measure for the privacy of instance encoding based on Fisher information. We show that our privacy measure is intuitive, easily applicable, and can be used to bound the invertibility of encodings both theoretically and empirically.

2305.16323

2023-05-06T07:50:12Z

Detecting Concept Drift for the reliability prediction of Software Defects using Instance Interpretation

In the context of Just-In-Time Software Defect Prediction (JIT-SDP), Concept drift (CD) can occur due to changes in the software development process, the complexity of the software, or changes in user behavior that may affect the stability of the JIT-SDP model over time. Additionally, the challenge of class imbalance in JIT-SDP data poses a potential risk to the accuracy of CD detection methods if rebalancing is implemented. This issue has not been explored to the best of our knowledge. Furthermore, methods to check the stability of JIT-SDP models over time by considering labeled evaluation data have been proposed. However, it should be noted that future data labels may not always be available promptly. We aim to develop a reliable JIT-SDP model using CD point detection directly by identifying changes in the interpretation of unlabeled simplified and resampled data. To evaluate our approach, we first obtained baseline methods based on model performance monitoring to identify CD points on labeled data. We then compared the output of the proposed methods with baseline methods based on performance monitoring of threshold-dependent and threshold-independent criteria using well-known performance measures in CD detection methods, such as accuracy, MDR, MTD, MTFA, and MTR. We also utilize the Friedman statistical test to assess the effectiveness of our approach. As a result, our proposed methods show higher compatibility with baseline methods based on threshold-independent criteria when applied to rebalanced data, and with baseline methods based on threshold-dependent criteria when applied to simple data.

2305.03914

2023-05-06T03:34:39Z

Variational Nonlinear Kalman Filtering with Unknown Process Noise Covariance

Motivated by the maneuvering target tracking with sensors such as radar and sonar, this paper considers the joint and recursive estimation of the dynamic state and the time-varying process noise covariance in nonlinear state space models. Due to the nonlinearity of the models and the non-conjugate prior, the state estimation problem is generally intractable as it involves integrals of general nonlinear functions and unknown process noise covariance, resulting in the posterior probability distribution functions lacking closed-form solutions. This paper presents a recursive solution for joint nonlinear state estimation and model parameters identification based on the approximate Bayesian inference principle. The stochastic search variational inference is adopted to offer a flexible, accurate, and effective approximation of the posterior distributions. We make two contributions compared to existing variational inference-based noise adaptive filtering methods. First, we introduce an auxiliary latent variable to decouple the latent variables of dynamic state and process noise covariance, thereby improving the flexibility of the posterior inference. Second, we split the variational lower bound optimization into conjugate and non-conjugate parts, whereas the conjugate terms are directly optimized that admit a closed-form solution and the non-conjugate terms are optimized by natural gradients, achieving the trade-off between inference speed and accuracy. The performance of the proposed method is verified on radar target tracking applications by both simulated and real-world data.

2305.03920

2023-05-06T03:52:33Z

Automated Spatio-Temporal Graph Contrastive Learning

Among various region embedding methods, graph-based region relation learning models stand out, owing to their strong structure representation ability for encoding spatial correlations with graph neural networks. Despite their effectiveness, several key challenges have not been well addressed in existing methods: i) Data noise and missing are ubiquitous in many spatio-temporal scenarios due to a variety of factors. ii) Input spatio-temporal data (e.g., mobility traces) usually exhibits distribution heterogeneity across space and time. In such cases, current methods are vulnerable to the quality of the generated region graphs, which may lead to suboptimal performance. In this paper, we tackle the above challenges by exploring the Automated Spatio-Temporal graph contrastive learning paradigm (AutoST) over the heterogeneous region graph generated from multi-view data sources. Our \model\ framework is built upon a heterogeneous graph neural architecture to capture the multi-view region dependencies with respect to POI semantics, mobility flow patterns and geographical positions. To improve the robustness of our GNN encoder against data noise and distribution issues, we design an automated spatio-temporal augmentation scheme with a parameterized contrastive view generator. AutoST can adapt to the spatio-temporal heterogeneous graph with multi-view semantics well preserved. Extensive experiments for three downstream spatio-temporal mining tasks on several real-world datasets demonstrate the significant performance gain achieved by our \model\ over a variety of baselines. The code is publicly available at https://github.com/HKUDS/AutoST.

2305.04034

2023-05-06T12:48:17Z

Wasserstein-Fisher-Rao Embedding: Logical Query Embeddings with Local Comparison and Global Transport

Answering complex queries on knowledge graphs is important but particularly challenging because of the data incompleteness. Query embedding methods address this issue by learning-based models and simulating logical reasoning with set operators. Previous works focus on specific forms of embeddings, but scoring functions between embeddings are underexplored. In contrast to existing scoring functions motivated by local comparison or global transport, this work investigates the local and global trade-off with unbalanced optimal transport theory. Specifically, we embed sets as bounded measures in $\real$ endowed with a scoring function motivated by the Wasserstein-Fisher-Rao metric. Such a design also facilitates closed-form set operators in the embedding space. Moreover, we introduce a convolution-based algorithm for linear time computation and a block-diagonal kernel to enforce the trade-off. Results show that WFRE can outperform existing query embedding methods on standard datasets, evaluation sets with combinatorially complex queries, and hierarchical knowledge graphs. Ablation study shows that finding a better local and global trade-off is essential for performance improvement.

2305.04059

2023-05-06T14:14:48Z

Decentralised Semi-supervised Onboard Learning for Scene Classification in Low-Earth Orbit

Onboard machine learning on the latest satellite hardware offers the potential for significant savings in communication and operational costs. We showcase the training of a machine learning model on a satellite constellation for scene classification using semi-supervised learning while accounting for operational constraints such as temperature and limited power budgets based on satellite processor benchmarks of the neural network. We evaluate mission scenarios employing both decentralised and federated learning approaches. All scenarios achieve convergence to high accuracy (around 91% on EuroSAT RGB dataset) within a one-day mission timeframe.

2305.03884

2023-05-06T00:43:36Z

On High-dimensional and Low-rank Tensor Bandits

Most existing studies on linear bandits focus on the one-dimensional characterization of the overall system. While being representative, this formulation may fail to model applications with high-dimensional but favorable structures, such as the low-rank tensor representation for recommender systems. To address this limitation, this work studies a general tensor bandits model, where actions and system parameters are represented by tensors as opposed to vectors, and we particularly focus on the case that the unknown system tensor is low-rank. A novel bandit algorithm, coined TOFU (Tensor Optimism in the Face of Uncertainty), is developed. TOFU first leverages flexible tensor regression techniques to estimate low-dimensional subspaces associated with the system tensor. These estimates are then utilized to convert the original problem to a new one with norm constraints on its system parameters. Lastly, a norm-constrained bandit subroutine is adopted by TOFU, which utilizes these constraints to avoid exploring the entire high-dimensional parameter space. Theoretical analyses show that TOFU improves the best-known regret upper bound by a multiplicative factor that grows exponentially in the system order. A novel performance lower bound is also established, which further corroborates the efficiency of TOFU.

2305.04148

2023-05-06T23:34:13Z

Efficient information recovery from Pauli noise via classical shadow

The rapid advancement of quantum computing has led to an extensive demand for effective techniques to extract classical information from quantum systems, particularly in fields like quantum machine learning and quantum chemistry. However, quantum systems are inherently susceptible to noises, which adversely corrupt the information encoded in quantum systems. In this work, we introduce an efficient algorithm that can recover information from quantum states under Pauli noise. The core idea is to learn the necessary information of the unknown Pauli channel by post-processing the classical shadows of the channel. For a local and bounded-degree observable, only partial knowledge of the channel is required rather than its complete classical description to recover the ideal information, resulting in a polynomial-time algorithm. This contrasts with conventional methods such as probabilistic error cancellation, which requires the full information of the channel and exhibits exponential scaling with the number of qubits. We also prove that this scalable method is optimal on the sample complexity and generalise the algorithm to the weight contracting channel. Furthermore, we demonstrate the validity of the algorithm on the 1D anisotropic Heisenberg-type model via numerical simulations. As a notable application, our method can be severed as a sample-efficient error mitigation scheme for Clifford circuits.

2305.05529

2023-05-06T23:52:16Z

Accelerate Langevin Sampling with Birth-Death process and Exploration Component

Sampling a probability distribution with known likelihood is a fundamental task in computational science and engineering. Aiming at multimodality, we propose a new sampling method that takes advantage of both birth-death process and exploration component. The main idea of this method is \textit{look before you leap}. We keep two sets of samplers, one at warmer temperature and one at original temperature. The former one serves as pioneer in exploring new modes and passing useful information to the other, while the latter one samples the target distribution after receiving the information. We derive a mean-field limit and show how the exploration process determines sampling efficiency. Moreover, we prove exponential asymptotic convergence under mild assumption. Finally, we test on experiments from previous literature and compared our methodology to previous ones.

2305.04232

2023-05-07T09:39:12Z

CatFLW: Cat Facial Landmarks in the Wild Dataset

Animal affective computing is a quickly growing field of research, where only recently first efforts to go beyond animal tracking into recognizing their internal states, such as pain and emotions, have emerged. In most mammals, facial expressions are an important channel for communicating information about these states. However, unlike the human domain, there is an acute lack of datasets that make automation of facial analysis of animals feasible. This paper aims to fill this gap by presenting a dataset called Cat Facial Landmarks in the Wild (CatFLW) which contains 2016 images of cat faces in different environments and conditions, annotated with 48 facial landmarks specifically chosen for their relationship with underlying musculature, and relevance to cat-specific facial Action Units (CatFACS). To the best of our knowledge, this dataset has the largest amount of cat facial landmarks available. In addition, we describe a semi-supervised (human-in-the-loop) method of annotating images with landmarks, used for creating this dataset, which significantly reduces the annotation time and could be used for creating similar datasets for other animals. The dataset is available on request.

2305.04268

2023-05-07T13:11:07Z

Multi-Space Neural Radiance Fields

Existing Neural Radiance Fields (NeRF) methods suffer from the existence of reflective objects, often resulting in blurry or distorted rendering. Instead of calculating a single radiance field, we propose a multi-space neural radiance field (MS-NeRF) that represents the scene using a group of feature fields in parallel sub-spaces, which leads to a better understanding of the neural network toward the existence of reflective and refractive objects. Our multi-space scheme works as an enhancement to existing NeRF methods, with only small computational overheads needed for training and inferring the extra-space outputs. We demonstrate the superiority and compatibility of our approach using three representative NeRF-based models, i.e., NeRF, Mip-NeRF, and Mip-NeRF 360. Comparisons are performed on a novelly constructed dataset consisting of 25 synthetic scenes and 7 real captured scenes with complex reflection and refraction, all having 360-degree viewpoints. Extensive experiments show that our approach significantly outperforms the existing single-space NeRF methods for rendering high-quality scenes concerned with complex light paths through mirror-like objects. Our code and dataset will be publicly available at https://zx-yin.github.io/msnerf.

2305.04328

2023-05-07T16:51:34Z

Neural Voting Field for Camera-Space 3D Hand Pose Estimation

We present a unified framework for camera-space 3D hand pose estimation from a single RGB image based on 3D implicit representation. As opposed to recent works, most of which first adopt holistic or pixel-level dense regression to obtain relative 3D hand pose and then follow with complex second-stage operations for 3D global root or scale recovery, we propose a novel unified 3D dense regression scheme to estimate camera-space 3D hand pose via dense 3D point-wise voting in camera frustum. Through direct dense modeling in 3D domain inspired by Pixel-aligned Implicit Functions for 3D detailed reconstruction, our proposed Neural Voting Field (NVF) fully models 3D dense local evidence and hand global geometry, helping to alleviate common 2D-to-3D ambiguities. Specifically, for a 3D query point in camera frustum and its pixel-aligned image feature, NVF, represented by a Multi-Layer Perceptron, regresses: (i) its signed distance to the hand surface; (ii) a set of 4D offset vectors (1D voting weight and 3D directional vector to each hand joint). Following a vote-casting scheme, 4D offset vectors from near-surface points are selected to calculate the 3D hand joint coordinates by a weighted average. Experiments demonstrate that NVF outperforms existing state-of-the-art algorithms on FreiHAND dataset for camera-space 3D hand pose estimation. We also adapt NVF to the classic task of root-relative 3D hand pose estimation, for which NVF also obtains state-of-the-art results on HO3D dataset.

2305.04332

2023-05-07T17:02:58Z

Segmentation of the veterinary cytological images for fast neoplastic tumors diagnosis

This paper shows the machine learning system which performs instance segmentation of cytological images in veterinary medicine. Eleven cell types were used directly and indirectly in the experiments, including damaged and unrecognized categories. The deep learning models employed in the system achieve a high score of average precision and recall metrics, i.e. 0.94 and 0.8 respectively, for the selected three types of tumors. This variety of label types allowed us to draw a meaningful conclusion that there are relatively few mistakes for tumor cell types. Additionally, the model learned tumor cell features well enough to avoid misclassification mistakes of one tumor type into another. The experiments also revealed that the quality of the results improves with the dataset size (excluding the damaged cells). It is worth noting that all the experiments were done using a custom dedicated dataset provided by the cooperating vet doctors.

2305.04374

2023-05-07T20:36:29Z

Spatiotemporally Consistent HDR Indoor Lighting Estimation

We propose a physically-motivated deep learning framework to solve a general version of the challenging indoor lighting estimation problem. Given a single LDR image with a depth map, our method predicts spatially consistent lighting at any given image position. Particularly, when the input is an LDR video sequence, our framework not only progressively refines the lighting prediction as it sees more regions, but also preserves temporal consistency by keeping the refinement smooth. Our framework reconstructs a spherical Gaussian lighting volume (SGLV) through a tailored 3D encoder-decoder, which enables spatially consistent lighting prediction through volume ray tracing, a hybrid blending network for detailed environment maps, an in-network Monte-Carlo rendering layer to enhance photorealism for virtual object insertion, and recurrent neural networks (RNN) to achieve temporally consistent lighting prediction with a video sequence as the input. For training, we significantly enhance the OpenRooms public dataset of photorealistic synthetic indoor scenes with around 360K HDR environment maps of much higher resolution and 38K video sequences, rendered with GPU-based path tracing. Experiments show that our framework achieves lighting prediction with higher quality compared to state-of-the-art single-image or video-based methods, leading to photorealistic AR applications such as object insertion.

2305.04156

2023-05-07T01:37:46Z

SynthMix: Mixing up Aligned Synthesis for Medical Cross-Modality Domain Adaptation

The adversarial methods showed advanced performance by producing synthetic images to mitigate the domain shift, a common problem due to the hardship of acquiring labelled data in medical field. Most existing studies focus on modifying the network architecture, but little has worked on the GAN training strategy. In this work, we propose SynthMix, an add-on module with a natural yet effective training policy that can promote synthetic quality without altering the network architecture. Following the adversarial philosophy of GAN, we designed a mix-up synthesis scheme termed SynthMix. It coherently mixed up aligned images of real and synthetic samples to stimulate the generation of fine-grained features, examined by an associated Inspector for the domain-specific details. We evaluated our method on two segmentation benchmarks among three publicly available datasets, where our method showed a significant performance gain compared with existing state-of-the-art approaches.

2305.04208

2023-05-07T07:26:41Z

Segmentation and Vascular Vectorization for Coronary Artery by Geometry-based Cascaded Neural Network

Segmentation of the coronary artery is an important task for the quantitative analysis of coronary computed tomography angiography (CCTA) images and is being stimulated by the field of deep learning. However, the complex structures with tiny and narrow branches of the coronary artery bring it a great challenge. Coupled with the medical image limitations of low resolution and poor contrast, fragmentations of segmented vessels frequently occur in the prediction. Therefore, a geometry-based cascaded segmentation method is proposed for the coronary artery, which has the following innovations: 1) Integrating geometric deformation networks, we design a cascaded network for segmenting the coronary artery and vectorizing results. The generated meshes of the coronary artery are continuous and accurate for twisted and sophisticated coronary artery structures, without fragmentations. 2) Different from mesh annotations generated by the traditional marching cube method from voxel-based labels, a finer vectorized mesh of the coronary artery is reconstructed with the regularized morphology. The novel mesh annotation benefits the geometry-based segmentation network, avoiding bifurcation adhesion and point cloud dispersion in intricate branches. 3) A dataset named CCA-200 is collected, consisting of 200 CCTA images with coronary artery disease. The ground truths of 200 cases are coronary internal diameter annotations by professional radiologists. Extensive experiments verify our method on our collected dataset CCA-200 and public ASOCA dataset, with a Dice of 0.778 on CCA-200 and 0.895 on ASOCA, showing superior results. Especially, our geometry-based model generates an accurate, intact and smooth coronary artery, devoid of any fragmentations of segmented vessels.

2305.04239

2023-05-07T10:12:14Z

Instance-Variant Loss with Gaussian RBF Kernel for 3D Cross-modal Retriveal

3D cross-modal retrieval is gaining attention in the multimedia community. Central to this topic is learning a joint embedding space to represent data from different modalities, such as images, 3D point clouds, and polygon meshes, to extract modality-invariant and discriminative features. Hence, the performance of cross-modal retrieval methods heavily depends on the representational capacity of this embedding space. Existing methods treat all instances equally, applying the same penalty strength to instances with varying degrees of difficulty, ignoring the differences between instances. This can result in ambiguous convergence or local optima, severely compromising the separability of the feature space. To address this limitation, we propose an Instance-Variant loss to assign different penalty strengths to different instances, improving the space separability. Specifically, we assign different penalty weights to instances positively related to their intra-class distance. Simultaneously, we reduce the cross-modal discrepancy between features by learning a shared weight vector for the same class data from different modalities. By leveraging the Gaussian RBF kernel to evaluate sample similarity, we further propose an Intra-Class loss function that minimizes the intra-class distance among same-class instances. Extensive experiments on three 3D cross-modal datasets show that our proposed method surpasses recent state-of-the-art approaches.

2305.04269

2023-05-07T13:11:55Z

Dual Residual Attention Network for Image Denoising

In image denoising, deep convolutional neural networks (CNNs) can obtain favorable performance on removing spatially invariant noise. However, many of these networks cannot perform well on removing the real noise (i.e. spatially variant noise) generated during image acquisition or transmission, which severely sets back their application in practical image denoising tasks. Instead of continuously increasing the network depth, many researchers have revealed that expanding the width of networks can also be a useful way to improve model performance. It also has been verified that feature filtering can promote the learning ability of the models. Therefore, in this paper, we propose a novel Dual-branch Residual Attention Network (DRANet) for image denoising, which has both the merits of a wide model architecture and attention-guided feature learning. The proposed DRANet includes two different parallel branches, which can capture complementary features to enhance the learning ability of the model. We designed a new residual attention block (RAB) and a novel hybrid dilated residual attention block (HDRAB) for the upper and the lower branches, respectively. The RAB and HDRAB can capture rich local features through multiple skip connections between different convolutional layers, and the unimportant features are dropped by the residual attention modules. Meanwhile, the long skip connections in each branch, and the global feature fusion between the two parallel branches can capture the global features as well. Moreover, the proposed DRANet uses downsampling operations and dilated convolutions to increase the size of the receptive field, which can enable DRANet to capture more image context information. Extensive experiments demonstrate that compared with other state-of-the-art denoising methods, our DRANet can produce competitive denoising performance both on synthetic and real-world noise removal.

2305.04275

2023-05-07T13:36:54Z

RSC-VAE: Recoding Semantic Consistency Based VAE for One-Class Novelty Detection

In recent years, there is an increasing interests in reconstruction based generative models for image One-Class Novelty Detection, most of which only focus on image-level information. While in this paper, we further exploit the latent space of Variational Auto-encoder (VAE), a typical reconstruction based model, and we innovatively divide it into three regions: Normal/Anomalous/Unknown-semantic-region. Based on this hypothesis, we propose a new VAE architecture, Recoding Semantic Consistency Based VAE (RSC-VAE), combining VAE with recoding mechanism and constraining the semantic consistency of two encodings. We come up with three training modes of RSC-VAE: 1. One-Class Training Mode, alleviating False Positive problem of normal samples; 2. Distributionally-Shifted Training Mode, alleviating False Negative problem of anomalous samples; 3. Extremely-Imbalanced Training Mode, introducing a small number of anomalous samples for training to enhance the second mode. The experimental results on multiple datasets demonstrate that our mechanism achieves state-of-the-art performance in various baselines including VAE.

2305.04296

2023-05-07T14:53:45Z

HashCC: Lightweight Method to Improve the Quality of the Camera-less NeRF Scene Generation

Neural Radiance Fields has become a prominent method of scene generation via view synthesis. A critical requirement for the original algorithm to learn meaningful scene representation is camera pose information for each image in a data set. Current approaches try to circumnavigate this assumption with moderate success, by learning approximate camera positions alongside learning neural representations of a scene. This requires complicated camera models, causing a long and complicated training process, or results in a lack of texture and sharp details in rendered scenes. In this work we introduce Hash Color Correction (HashCC) -- a lightweight method for improving Neural Radiance Fields rendered image quality, applicable also in situations where camera positions for a given set of images are unknown.

2305.04298

2023-05-07T14:57:58Z

Poses as Queries: Image-to-LiDAR Map Localization with Transformers

High-precision vehicle localization with commercial setups is a crucial technique for high-level autonomous driving tasks. Localization with a monocular camera in LiDAR map is a newly emerged approach that achieves promising balance between cost and accuracy, but estimating pose by finding correspondences between such cross-modal sensor data is challenging, thereby damaging the localization accuracy. In this paper, we address the problem by proposing a novel Transformer-based neural network to register 2D images into 3D LiDAR map in an end-to-end manner. Poses are implicitly represented as high-dimensional feature vectors called pose queries and can be iteratively updated by interacting with the retrieved relevant information from cross-model features using attention mechanism in a proposed POse Estimator Transformer (POET) module. Moreover, we apply a multiple hypotheses aggregation method that estimates the final poses by performing parallel optimization on multiple randomly initialized pose queries to reduce the network uncertainty. Comprehensive analysis and experimental results on public benchmark conclude that the proposed image-to-LiDAR map localization network could achieve state-of-the-art performances in challenging cross-modal localization tasks.

2305.05542

2023-05-07T19:20:42Z

Localization of Ultra-dense Emitters with Neural Networks

Single-Molecule Localization Microscopy (SMLM) has expanded our ability to visualize subcellular structures but is limited in its temporal resolution. Increasing emitter density will improve temporal resolution, but current analysis algorithms struggle as emitter images significantly overlap. Here we present a deep convolutional neural network called LUENN which utilizes a unique architecture that rejects the isolated emitter assumption; it can smoothly accommodate emitters that range from completely isolated to co-located. This architecture, alongside an accurate estimator of location uncertainty, extends the range of usable emitter densities by a factor of 6 to over 31 emitters per micrometer-squared with reduced penalty to localization precision and improved temporal resolution. Apart from providing uncertainty estimation, the algorithm improves usability in laboratories by reducing imaging times and easing requirements for successful experiments.

2305.04183

2023-05-07T03:59:31Z

OpenViVQA: Task, Dataset, and Multimodal Fusion Models for Visual Question Answering in Vietnamese

In recent years, visual question answering (VQA) has attracted attention from the research community because of its highly potential applications (such as virtual assistance on intelligent cars, assistant devices for blind people, or information retrieval from document images using natural language as queries) and challenge. The VQA task requires methods that have the ability to fuse the information from questions and images to produce appropriate answers. Neural visual question answering models have achieved tremendous growth on large-scale datasets which are mostly for resource-rich languages such as English. However, available datasets narrow the VQA task as the answers selection task or answer classification task. We argue that this form of VQA is far from human ability and eliminates the challenge of the answering aspect in the VQA task by just selecting answers rather than generating them. In this paper, we introduce the OpenViVQA (Open-domain Vietnamese Visual Question Answering) dataset, the first large-scale dataset for VQA with open-ended answers in Vietnamese, consists of 11,000+ images associated with 37,000+ question-answer pairs (QAs). Moreover, we proposed FST, QuMLAG, and MLPAG which fuse information from images and answers, then use these fused features to construct answers as humans iteratively. Our proposed methods achieve results that are competitive with SOTA models such as SAAA, MCAN, LORA, and M4C. The dataset is available to encourage the research community to develop more generalized algorithms including transformers for low-resource languages such as Vietnamese.

2305.04265

2023-05-07T13:03:17Z

An Investigation on Word Embedding Offset Clustering as Relationship Classification

Vector representations obtained from word embedding are the source of many groundbreaking advances in natural language processing. They yield word representations that are capable of capturing semantics and analogies of words within a text corpus. This study is an investigation in an attempt to elicit a vector representation of relationships between pairs of word vectors. We use six pooling strategies to represent vector relationships. Different types of clustering models are applied to analyze which one correctly groups relationship types. Subtraction pooling coupled with a centroid based clustering mechanism shows better performances in our experimental setup. This work aims to provide directions for a word embedding based unsupervised method to identify the nature of a relationship represented by a pair of words.

2305.04297

2023-05-07T14:57:42Z

HIORE: Leveraging High-order Interactions for Unified Entity Relation Extraction

Entity relation extraction consists of two sub-tasks: entity recognition and relation extraction. Existing methods either tackle these two tasks separately or unify them with word-by-word interactions. In this paper, we propose HIORE, a new method for unified entity relation extraction. The key insight is to leverage the high-order interactions, i.e., the complex association among word pairs, which contains richer information than the first-order word-by-word interactions. For this purpose, we first devise a W-shape DNN (WNet) to capture coarse-level high-order connections. Then, we build a heuristic high-order graph and further calibrate the representations with a graph neural network (GNN). Experiments on three benchmarks (ACE04, ACE05, SciERC) show that HIORE achieves the state-of-the-art performance on relation extraction and an improvement of 1.1~1.8 F1 points over the prior best unified model.

2305.04344

2023-05-07T17:45:47Z

Empowering Language Model with Guided Knowledge Fusion for Biomedical Document Re-ranking

Pre-trained language models (PLMs) have proven to be effective for document re-ranking task. However, they lack the ability to fully interpret the semantics of biomedical and health-care queries and often rely on simplistic patterns for retrieving documents. To address this challenge, we propose an approach that integrates knowledge and the PLMs to guide the model toward effectively capturing information from external sources and retrieving the correct documents. We performed comprehensive experiments on two biomedical and open-domain datasets that show that our approach significantly improves vanilla PLMs and other existing approaches for document re-ranking task.

2305.04365

2023-05-07T19:59:01Z

LatinCy: Synthetic Trained Pipelines for Latin NLP

This paper introduces LatinCy, a set of trained general purpose Latin-language "core" pipelines for use with the spaCy natural language processing framework. The models are trained on a large amount of available Latin data, including all five of the Latin Universal Dependency treebanks, which have been preprocessed to be compatible with each other. The result is a set of general models for Latin with good performance on a number of natural language processing tasks (e.g. the top-performing model yields POS tagging, 97.41% accuracy; lemmatization, 94.66% accuracy; morphological tagging 92.76% accuracy). The paper describes the model training, including its training data and parameterization, and presents the advantages to Latin-language researchers of having a spaCy model available for NLP work.

2305.04177

2023-05-07T03:29:55Z

MIReAD: Simple Method for Learning High-quality Representations from Scientific Documents

Learning semantically meaningful representations from scientific documents can facilitate academic literature search and improve performance of recommendation systems. Pre-trained language models have been shown to learn rich textual representations, yet they cannot provide powerful document-level representations for scientific articles. We propose MIReAD, a simple method that learns high-quality representations of scientific papers by fine-tuning transformer model to predict the target journal class based on the abstract. We train MIReAD on more than 500,000 PubMed and arXiv abstracts across over 2,000 journal classes. We show that MIReAD produces representations that can be used for similar papers retrieval, topic categorization and literature search. Our proposed approach outperforms six existing models for representation learning on scientific documents across four evaluation standards.

2305.04181

2023-05-07T03:47:05Z

Shall We Trust All Relational Tuples by Open Information Extraction? A Study on Speculation Detection

Open Information Extraction (OIE) aims to extract factual relational tuples from open-domain sentences. Downstream tasks use the extracted OIE tuples as facts, without examining the certainty of these facts. However, uncertainty/speculation is a common linguistic phenomenon. Existing studies on speculation detection are defined at sentence level, but even if a sentence is determined to be speculative, not all tuples extracted from it may be speculative. In this paper, we propose to study speculations in OIE and aim to determine whether an extracted tuple is speculative. We formally define the research problem of tuple-level speculation detection and conduct a detailed data analysis on the LSOIE dataset which contains labels for speculative tuples. Lastly, we propose a baseline model OIE-Spec for this new research task.

2305.04346

2023-05-07T17:53:08Z

Laziness Is a Virtue When It Comes to Compositionality in Neural Semantic Parsing

Nearly all general-purpose neural semantic parsers generate logical forms in a strictly top-down autoregressive fashion. Though such systems have achieved impressive results across a variety of datasets and domains, recent works have called into question whether they are ultimately limited in their ability to compositionally generalize. In this work, we approach semantic parsing from, quite literally, the opposite direction; that is, we introduce a neural semantic parsing generation method that constructs logical forms from the bottom up, beginning from the logical form's leaves. The system we introduce is lazy in that it incrementally builds up a set of potential semantic parses, but only expands and processes the most promising candidate parses at each generation step. Such a parsimonious expansion scheme allows the system to maintain an arbitrarily large set of parse hypotheses that are never realized and thus incur minimal computational overhead. We evaluate our approach on compositional generalization; specifically, on the challenging CFQ dataset and three Text-to-SQL datasets where we show that our novel, bottom-up semantic parsing technique outperforms general-purpose semantic parsers while also being competitive with comparable neural parsers that have been designed for each task.

2305.04356

2023-05-07T18:58:54Z

Stanford MLab at SemEval-2023 Task 10: Exploring GloVe- and Transformer-Based Methods for the Explainable Detection of Online Sexism

In this paper, we discuss the methods we applied at SemEval-2023 Task 10: Towards the Explainable Detection of Online Sexism. Given an input text, we perform three classification tasks to predict whether the text is sexist and classify the sexist text into subcategories in order to provide an additional explanation as to why the text is sexist. We explored many different types of models, including GloVe embeddings as the baseline approach, transformer-based deep learning models like BERT, RoBERTa, and DeBERTa, ensemble models, and model blending. We explored various data cleaning and augmentation methods to improve model performance. Pre-training transformer models yielded significant improvements in performance, and ensembles and blending slightly improved robustness in the F1 score.

2305.06155

2023-05-07T07:42:22Z

Leveraging Synthetic Targets for Machine Translation

In this work, we provide a recipe for training machine translation models in a limited resource setting by leveraging synthetic target data generated using a large pre-trained model. We show that consistently across different benchmarks in bilingual, multilingual, and speech translation setups, training models on synthetic targets outperforms training on the actual ground-truth data. This performance gap grows bigger with increasing limits on the amount of available resources in the form of the size of the dataset and the number of parameters in the model. We also provide preliminary analysis into whether this boost in performance is linked to ease of optimization or more deterministic nature of the predictions, and whether this paradigm leads to better out-of-distribution performance across different testing domains.

2305.06273

2023-05-07T15:10:59Z

Learning Robust Self-attention Features for Speech Emotion Recognition with Label-adaptive Mixup

Speech Emotion Recognition (SER) is to recognize human emotions in a natural verbal interaction scenario with machines, which is considered as a challenging problem due to the ambiguous human emotions. Despite the recent progress in SER, state-of-the-art models struggle to achieve a satisfactory performance. We propose a self-attention based method with combined use of label-adaptive mixup and center loss. By adapting label probabilities in mixup and fitting center loss to the mixup training scheme, our proposed method achieves a superior performance to the state-of-the-art methods.

2305.10408

2023-05-07T00:16:30Z

Extracting Blockchain Concepts from Text

Blockchains provide a mechanism through which mutually distrustful remote parties can reach consensus on the state of a ledger of information. With the great acceleration with which this space is developed, the demand for those seeking to learn about blockchain also grows. Being a technical subject, it can be quite intimidating to start learning. For this reason, the main objective of this project was to apply machine learning models to extract information from whitepapers and academic articles focused on the blockchain area to organize this information and aid users to navigate the space.

2305.04201

2023-05-07T06:46:35Z

MrTF: Model Refinery for Transductive Federated Learning

We consider a real-world scenario in which a newly-established pilot project needs to make inferences for newly-collected data with the help of other parties under privacy protection policies. Current federated learning (FL) paradigms are devoted to solving the data heterogeneity problem without considering the to-be-inferred data. We propose a novel learning paradigm named transductive federated learning (TFL) to simultaneously consider the structural information of the to-be-inferred data. On the one hand, the server could use the pre-available test samples to refine the aggregated models for robust model fusion, which tackles the data heterogeneity problem in FL. On the other hand, the refinery process incorporates test samples into training and could generate better predictions in a transductive manner. We propose several techniques including stabilized teachers, rectified distillation, and clustered label refinery to facilitate the model refinery process. Abundant experimental studies verify the superiorities of the proposed \underline{M}odel \underline{r}efinery framework for \underline{T}ransductive \underline{F}ederated learning (MrTF). The source code is available at \url{https://github.com/lxcnju/MrTF}.

2305.10432

2023-05-07T23:48:03Z

Model-Contrastive Federated Domain Adaptation

Federated domain adaptation (FDA) aims to collaboratively transfer knowledge from source clients (domains) to the related but different target client, without communicating the local data of any client. Moreover, the source clients have different data distributions, leading to extremely challenging in knowledge transfer. Despite the recent progress in FDA, we empirically find that existing methods can not leverage models of heterogeneous domains and thus they fail to achieve excellent performance. In this paper, we propose a model-based method named FDAC, aiming to address {\bf F}ederated {\bf D}omain {\bf A}daptation based on {\bf C}ontrastive learning and Vision Transformer (ViT). In particular, contrastive learning can leverage the unlabeled data to train excellent models and the ViT architecture performs better than convolutional neural networks (CNNs) in extracting adaptable features. To the best of our knowledge, FDAC is the first attempt to learn transferable representations by manipulating the latent architecture of ViT under the federated setting. Furthermore, FDAC can increase the target data diversity by compensating from each source model with insufficient knowledge of samples and features, based on domain augmentation and semantic matching. Extensive experiments on several real datasets demonstrate that FDAC outperforms all the comparative methods in most conditions. Moreover, FDCA can also improve communication efficiency which is another key factor in the federated setting.

2305.04325

2023-05-07T16:43:52Z

Lightweight Convolution Transformer for Cross-patient Seizure Detection in Multi-channel EEG Signals

Background: Epilepsy is a neurological illness affecting the brain that makes people more likely to experience frequent, spontaneous seizures. There has to be an accurate automated method for measuring seizure frequency and severity in order to assess the efficacy of pharmacological therapy for epilepsy. The drug quantities are often derived from patient reports which may cause significant issues owing to inadequate or inaccurate descriptions of seizures and their frequencies. Methods and materials: This study proposes a novel deep learning architecture based lightweight convolution transformer (LCT). The transformer is able to learn spatial and temporal correlated information simultaneously from the multi-channel electroencephalogram (EEG) signal to detect seizures at smaller segment lengths. In the proposed model, the lack of translation equivariance and localization of ViT is reduced using convolution tokenization, and rich information from the transformer encoder is extracted by sequence pooling instead of the learnable class token. Results: Extensive experimental results demonstrate that the proposed model of cross-patient learning can effectively detect seizures from the raw EEG signals. The accuracy and F1-score of seizure detection in the cross-patient case on the CHB-MIT dataset are shown to be 96.31% and 96.32%, respectively, at 0.5 sec segment length. In addition, the performance metrics show that the inclusion of inductive biases and attention-based pooling in the model enhances the performance and reduces the number of transformer encoder layers, which significantly reduces the computational complexity. In this research work, we provided a novel approach to enhance efficiency and simplify the architecture for multi-channel automated seizure detection.

2305.04361

2023-05-07T19:41:57Z

Truncating Trajectories in Monte Carlo Reinforcement Learning

In Reinforcement Learning (RL), an agent acts in an unknown environment to maximize the expected cumulative discounted sum of an external reward signal, i.e., the expected return. In practice, in many tasks of interest, such as policy optimization, the agent usually spends its interaction budget by collecting episodes of fixed length within a simulator (i.e., Monte Carlo simulation). However, given the discounted nature of the RL objective, this data collection strategy might not be the best option. Indeed, the rewards taken in early simulation steps weigh exponentially more than future rewards. Taking a cue from this intuition, in this paper, we design an a-priori budget allocation strategy that leads to the collection of trajectories of different lengths, i.e., truncated. The proposed approach provably minimizes the width of the confidence intervals around the empirical estimates of the expected return of a policy. After discussing the theoretical properties of our method, we make use of our trajectory truncation mechanism to extend Policy Optimization via Importance Sampling (POIS, Metelli et al., 2018) algorithm. Finally, we conduct a numerical comparison between our algorithm and POIS: the results are consistent with our theory and show that an appropriate truncation of the trajectories can succeed in improving performance.

2305.04364

2023-05-07T19:56:51Z

A Generalized Framework for Predictive Clustering and Optimization

Clustering is a powerful and extensively used data science tool. While clustering is generally thought of as an unsupervised learning technique, there are also supervised variations such as Spath's clusterwise regression that attempt to find clusters of data that yield low regression error on a supervised target. We believe that clusterwise regression is just a single vertex of a largely unexplored design space of supervised clustering models. In this article, we define a generalized optimization framework for predictive clustering that admits different cluster definitions (arbitrary point assignment, closest center, and bounding box) and both regression and classification objectives. We then present a joint optimization strategy that exploits mixed-integer linear programming (MILP) for global optimization in this generalized framework. To alleviate scalability concerns for large datasets, we also provide highly scalable greedy algorithms inspired by the Majorization-Minimization (MM) framework. Finally, we demonstrate the ability of our models to uncover different interpretable discrete cluster structures in data by experimenting with four real-world datasets.

2305.05377

2023-05-07T00:56:58Z

Professional Certification Benchmark Dataset: The First 500 Jobs For Large Language Models

The research creates a professional certification survey to test large language models and evaluate their employable skills. It compares the performance of two AI models, GPT-3 and Turbo-GPT3.5, on a benchmark dataset of 1149 professional certifications, emphasizing vocational readiness rather than academic performance. GPT-3 achieved a passing score (>70% correct) in 39% of the professional certifications without fine-tuning or exam preparation. The models demonstrated qualifications in various computer-related fields, such as cloud and virtualization, business analytics, cybersecurity, network setup and repair, and data analytics. Turbo-GPT3.5 scored 100% on the valuable Offensive Security Certified Professional (OSCP) exam. The models also displayed competence in other professional domains, including nursing, licensed counseling, pharmacy, and teaching. Turbo-GPT3.5 passed the Financial Industry Regulatory Authority (FINRA) Series 6 exam with a 70% grade without preparation. Interestingly, Turbo-GPT3.5 performed well on customer service tasks, suggesting potential applications in human augmentation for chatbots in call centers and routine advice services. The models also score well on sensory and experience-based tests such as wine sommelier, beer taster, emotional quotient, and body language reader. The OpenAI model improvement from Babbage to Turbo resulted in a median 60% better-graded performance in less than a few years. This progress suggests that focusing on the latest model's shortcomings could lead to a highly performant AI capable of mastering the most demanding professional certifications. We open-source the benchmark to expand the range of testable professional skills as the models improve or gain emergent capabilities.

2305.05548

2023-05-07T16:27:09Z

CIT-EmotionNet: CNN Interactive Transformer Network for EEG Emotion Recognition

Emotion recognition using Electroencephalogram (EEG) signals has emerged as a significant research challenge in affective computing and intelligent interaction. However, effectively combining global and local features of EEG signals to improve performance in emotion recognition is still a difficult task. In this study, we propose a novel CNN Interactive Transformer Network for EEG Emotion Recognition, known as CIT-EmotionNet, which efficiently integrates global and local features of EEG signals. Initially, we convert raw EEG signals into spatial-frequency representations, which serve as inputs. Then, we integrate Convolutional Neural Network (CNN) and Transformer within a single framework in a parallel manner. Finally, we design a CNN interactive Transformer module, which facilitates the interaction and fusion of local and global features, thereby enhancing the model's ability to extract both types of features from EEG spatial-frequency representations. The proposed CIT-EmotionNet outperforms state-of-the-art methods, achieving an average recognition accuracy of 98.57\% and 92.09\% on two publicly available datasets, SEED and SEED-IV, respectively.

2305.05668

2023-05-07T12:11:04Z

Neurosymbolic Artificial Intelligence (NSAI) based Algorithm for predicting the Impact Strength of Additive Manufactured Polylactic Acid (PLA) Specimens

In this study, we introduce application of Neurosymbolic Artificial Intelligence (NSAI) for predicting the impact strength of additive manufactured polylactic acid (PLA) components, representing the first-ever use of NSAI in the domain of additive manufacturing. The NSAI model amalgamates the advantages of neural networks and symbolic AI, offering a more robust and accurate prediction than traditional machine learning techniques. Experimental data was collected and synthetically augmented to 1000 data points, enhancing the model's precision. The Neurosymbolic model was developed using a neural network architecture comprising input, two hidden layers, and an output layer, followed by a decision tree regressor representing the symbolic component. The model's performance was benchmarked against a Simple Artificial Neural Network (ANN) model by assessing mean squared error (MSE) and R-squared (R2) values for both training and validation datasets. The results reveal that the Neurosymbolic model surpasses the Simple ANN model, attaining lower MSE and higher R2 values for both training and validation sets. This innovative application of the Neurosymbolic approach in estimating the impact strength of additive manufactured PLA components underscores its potential for optimizing the additive manufacturing process. Future research could investigate further refinements to the Neurosymbolic model, extend its application to other materials and additive manufacturing processes, and incorporate real-time monitoring and control for enhanced process optimization.

2305.13936

2023-05-07T14:57:08Z

Robust Multi-agent Communication via Multi-view Message Certification

Many multi-agent scenarios require message sharing among agents to promote coordination, hastening the robustness of multi-agent communication when policies are deployed in a message perturbation environment. Major relevant works tackle this issue under specific assumptions, like a limited number of message channels would sustain perturbations, limiting the efficiency in complex scenarios. In this paper, we take a further step addressing this issue by learning a robust multi-agent communication policy via multi-view message certification, dubbed CroMAC. Agents trained under CroMAC can obtain guaranteed lower bounds on state-action values to identify and choose the optimal action under a worst-case deviation when the received messages are perturbed. Concretely, we first model multi-agent communication as a multi-view problem, where every message stands for a view of the state. Then we extract a certificated joint message representation by a multi-view variational autoencoder (MVAE) that uses a product-of-experts inference network. For the optimization phase, we do perturbations in the latent space of the state for a certificate guarantee. Then the learned joint message representation is used to approximate the certificated state representation during training. Extensive experiments in several cooperative multi-agent benchmarks validate the effectiveness of the proposed CroMAC.

2305.13937

2023-05-07T15:04:56Z

Multi-agent Continual Coordination via Progressive Task Contextualization

Cooperative Multi-agent Reinforcement Learning (MARL) has attracted significant attention and played the potential for many real-world applications. Previous arts mainly focus on facilitating the coordination ability from different aspects (e.g., non-stationarity, credit assignment) in single-task or multi-task scenarios, ignoring the stream of tasks that appear in a continual manner. This ignorance makes the continual coordination an unexplored territory, neither in problem formulation nor efficient algorithms designed. Towards tackling the mentioned issue, this paper proposes an approach Multi-Agent Continual Coordination via Progressive Task Contextualization, dubbed MACPro. The key point lies in obtaining a factorized policy, using shared feature extraction layers but separated independent task heads, each specializing in a specific class of tasks. The task heads can be progressively expanded based on the learned task contextualization. Moreover, to cater to the popular CTDE paradigm in MARL, each agent learns to predict and adopt the most relevant policy head based on local information in a decentralized manner. We show in multiple multi-agent benchmarks that existing continual learning methods fail, while MACPro is able to achieve close-to-optimal performance. More results also disclose the effectiveness of MACPro from multiple aspects like high generalization ability.

2305.04267

2023-05-07T13:05:09Z

Provable Identifiability of Two-Layer ReLU Neural Networks via LASSO Regularization

LASSO regularization is a popular regression tool to enhance the prediction accuracy of statistical models by performing variable selection through the $\ell_1$ penalty, initially formulated for the linear model and its variants. In this paper, the territory of LASSO is extended to two-layer ReLU neural networks, a fashionable and powerful nonlinear regression model. Specifically, given a neural network whose output $y$ depends only on a small subset of input $\boldsymbol{x}$, denoted by $\mathcal{S}^{\star}$, we prove that the LASSO estimator can stably reconstruct the neural network and identify $\mathcal{S}^{\star}$ when the number of samples scales logarithmically with the input dimension. This challenging regime has been well understood for linear models while barely studied for neural networks. Our theory lies in an extended Restricted Isometry Property (RIP)-based analysis framework for two-layer ReLU neural networks, which may be of independent interest to other LASSO or neural network settings. Based on the result, we advocate a neural network-based variable selection method. Experiments on simulated and real-world datasets show promising performance of the variable selection approach compared with existing techniques.

2305.04279

2023-05-07T14:01:52Z

Boosting Distributed Machine Learning Training Through Loss-tolerant Transmission Protocol

Distributed Machine Learning (DML) systems are utilized to enhance the speed of model training in data centers (DCs) and edge nodes. The Parameter Server (PS) communication architecture is commonly employed, but it faces severe long-tail latency caused by many-to-one "incast" traffic patterns, negatively impacting training throughput. To address this challenge, we design the \textbf{L}oss-tolerant \textbf{T}ransmission \textbf{P}rotocol (LTP), which permits partial loss of gradients during synchronization to avoid unneeded retransmission and contributes to faster synchronization per iteration. LTP implements loss-tolerant transmission through \textit{out-of-order transmission} and \textit{out-of-order Acknowledges (ACKs)}. LTP employs \textit{Early Close} to adjust the loss-tolerant threshold based on network conditions and \textit{Bubble Filling} for data correction to maintain training accuracy. LTP is implemented by C++ and integrated into PyTorch. Evaluations on a testbed of 8 worker nodes and one PS node demonstrate that LTP can significantly improve DML training task throughput by up to 30x compared to traditional TCP congestion controls, with no sacrifice to final accuracy.

2305.04341

2023-05-07T17:40:52Z

Fast parameter estimation of Generalized Extreme Value distribution using Neural Networks

The heavy-tailed behavior of the generalized extreme-value distribution makes it a popular choice for modeling extreme events such as floods, droughts, heatwaves, wildfires, etc. However, estimating the distribution's parameters using conventional maximum likelihood methods can be computationally intensive, even for moderate-sized datasets. To overcome this limitation, we propose a computationally efficient, likelihood-free estimation method utilizing a neural network. Through an extensive simulation study, we demonstrate that the proposed neural network-based method provides Generalized Extreme Value (GEV) distribution parameter estimates with comparable accuracy to the conventional maximum likelihood method but with a significant computational speedup. To account for estimation uncertainty, we utilize parametric bootstrapping, which is inherent in the trained network. Finally, we apply this method to 1000-year annual maximum temperature data from the Community Climate System Model version 3 (CCSM3) across North America for three atmospheric concentrations: 289 ppm $\mathrm{CO}_2$ (pre-industrial), 700 ppm $\mathrm{CO}_2$ (future conditions), and 1400 ppm $\mathrm{CO}_2$, and compare the results with those obtained using the maximum likelihood approach.

2305.04347

2023-05-07T17:53:31Z

Interpreting Training Aspects of Deep-Learned Error-Correcting Codes

As new deep-learned error-correcting codes continue to be introduced, it is important to develop tools to interpret the designed codes and understand the training process. Prior work focusing on the deep-learned TurboAE has both interpreted the learned encoders post-hoc by mapping these onto nearby ``interpretable'' encoders, and experimentally evaluated the performance of these interpretable encoders with various decoders. Here we look at developing tools for interpreting the training process for deep-learned error-correcting codes, focusing on: 1) using the Goldreich-Levin algorithm to quickly interpret the learned encoder; 2) using Fourier coefficients as a tool for understanding the training dynamics and the loss landscape; 3) reformulating the training loss, the binary cross entropy, by relating it to encoder and decoder parameters, and the bit error rate (BER); 4) using these insights to formulate and study a new training procedure. All tools are demonstrated on TurboAE, but are applicable to other deep-learned forward error correcting codes (without feedback).

2305.05538

2023-05-07T16:05:30Z

Efficient pattern-based anomaly detection in a network of multivariate devices

Many organisations manage service quality and monitor a large set devices and servers where each entity is associated with telemetry or physical sensor data series. Recently, various methods have been proposed to detect behavioural anomalies, however existing approaches focus on multivariate time series and ignore communication between entities. Moreover, we aim to support end-users in not only in locating entities and sensors causing an anomaly at a certain period, but also explain this decision. We propose a scalable approach to detect anomalies using a two-step approach. First, we recover relations between entities in the network, since relations are often dynamic in nature and caused by an unknown underlying process. Next, we report anomalies based on an embedding of sequential patterns. Pattern mining is efficient and supports interpretation, i.e. patterns represent frequent occurring behaviour in time series. We extend pattern mining to filter sequential patterns based on frequency, temporal constraints and minimum description length. We collect and release two public datasets for international broadcasting and X from an Internet company. \textit{BAD} achieves an overall F1-Score of 0.78 on 9 benchmark datasets, significantly outperforming the best baseline by 3\%. Additionally, \textit{BAD} is also an order-of-magnitude faster than state-of-the-art anomaly detection methods.

2305.04396

2023-05-08T00:19:05Z

SegGPT Meets Co-Saliency Scene

Co-salient object detection targets at detecting co-existed salient objects among a group of images. Recently, a generalist model for segmenting everything in context, called SegGPT, is gaining public attention. In view of its breakthrough for segmentation, we can hardly wait to probe into its contribution to the task of co-salient object detection. In this report, we first design a framework to enable SegGPT for the problem of co-salient object detection. Proceed to the next step, we evaluate the performance of SegGPT on the problem of co-salient object detection on three available datasets. We achieve a finding that co-saliency scenes challenges SegGPT due to context discrepancy within a group of co-saliency images.

2305.04426

2023-05-08T02:33:59Z

Improving 2D face recognition via fine-level facial depth generation and RGB-D complementary feature learning

Face recognition in complex scenes suffers severe challenges coming from perturbations such as pose deformation, ill illumination, partial occlusion. Some methods utilize depth estimation to obtain depth corresponding to RGB to improve the accuracy of face recognition. However, the depth generated by them suffer from image blur, which introduces noise in subsequent RGB-D face recognition tasks. In addition, existing RGB-D face recognition methods are unable to fully extract complementary features. In this paper, we propose a fine-grained facial depth generation network and an improved multimodal complementary feature learning network. Extensive experiments on the Lock3DFace dataset and the IIIT-D dataset show that the proposed FFDGNet and I MCFLNet can improve the accuracy of RGB-D face recognition while achieving the state-of-the-art performance.

2305.04436

2023-05-08T03:14:01Z

Adversarial Examples Detection with Enhanced Image Difference Features based on Local Histogram Equalization

Deep Neural Networks (DNNs) have recently made significant progress in many fields. However, studies have shown that DNNs are vulnerable to adversarial examples, where imperceptible perturbations can greatly mislead DNNs even if the full underlying model parameters are not accessible. Various defense methods have been proposed, such as feature compression and gradient masking. However, numerous studies have proven that previous methods create detection or defense against certain attacks, which renders the method ineffective in the face of the latest unknown attack methods. The invisibility of adversarial perturbations is one of the evaluation indicators for adversarial example attacks, which also means that the difference in the local correlation of high-frequency information in adversarial examples and normal examples can be used as an effective feature to distinguish the two. Therefore, we propose an adversarial example detection framework based on a high-frequency information enhancement strategy, which can effectively extract and amplify the feature differences between adversarial examples and normal examples. Experimental results show that the feature augmentation module can be combined with existing detection models in a modular way under this framework. Improve the detector's performance and reduce the deployment cost without modifying the existing detection model.

2305.04441

2023-05-08T03:34:33Z

Prompt Tuning Inversion for Text-Driven Image Editing Using Diffusion Models

Recently large-scale language-image models (e.g., text-guided diffusion models) have considerably improved the image generation capabilities to generate photorealistic images in various domains. Based on this success, current image editing methods use texts to achieve intuitive and versatile modification of images. To edit a real image using diffusion models, one must first invert the image to a noisy latent from which an edited image is sampled with a target text prompt. However, most methods lack one of the following: user-friendliness (e.g., additional masks or precise descriptions of the input image are required), generalization to larger domains, or high fidelity to the input image. In this paper, we design an accurate and quick inversion technique, Prompt Tuning Inversion, for text-driven image editing. Specifically, our proposed editing method consists of a reconstruction stage and an editing stage. In the first stage, we encode the information of the input image into a learnable conditional embedding via Prompt Tuning Inversion. In the second stage, we apply classifier-free guidance to sample the edited image, where the conditional embedding is calculated by linearly interpolating between the target embedding and the optimized one obtained in the first stage. This technique ensures a superior trade-off between editability and high fidelity to the input image of our method. For example, we can change the color of a specific object while preserving its original shape and background under the guidance of only a target text prompt. Extensive experiments on ImageNet demonstrate the superior editing performance of our method compared to the state-of-the-art baselines.

2305.04451

2023-05-08T04:10:36Z

FashionTex: Controllable Virtual Try-on with Text and Texture

Virtual try-on attracts increasing research attention as a promising way for enhancing the user experience for online cloth shopping. Though existing methods can generate impressive results, users need to provide a well-designed reference image containing the target fashion clothes that often do not exist. To support user-friendly fashion customization in full-body portraits, we propose a multi-modal interactive setting by combining the advantages of both text and texture for multi-level fashion manipulation. With the carefully designed fashion editing module and loss functions, FashionTex framework can semantically control cloth types and local texture patterns without annotated pairwise training data. We further introduce an ID recovery module to maintain the identity of input portrait. Extensive experiments have demonstrated the effectiveness of our proposed pipeline.

2305.04457

2023-05-08T04:48:03Z

Real-World Denoising via Diffusion Model

Real-world image denoising is an extremely important image processing problem, which aims to recover clean images from noisy images captured in natural environments. In recent years, diffusion models have achieved very promising results in the field of image generation, outperforming previous generation models. However, it has not been widely used in the field of image denoising because it is difficult to control the appropriate position of the added noise. Inspired by diffusion models, this paper proposes a novel general denoising diffusion model that can be used for real-world image denoising. We introduce a diffusion process with linear interpolation, and the intermediate noisy image is interpolated from the original clean image and the corresponding real-world noisy image, so that this diffusion model can handle the level of added noise. In particular, we also introduce two sampling algorithms for this diffusion model. The first one is a simple sampling procedure defined according to the diffusion process, and the second one targets the problem of the first one and makes a number of improvements. Our experimental results show that our proposed method with a simple CNNs Unet achieves comparable results compared to the Transformer architecture. Both quantitative and qualitative evaluations on real-world denoising benchmarks show that the proposed general diffusion model performs almost as well as against the state-of-the-art methods.

2305.04524

2023-05-08T07:47:49Z

Scene Text Recognition with Image-Text Matching-guided Dictionary

Employing a dictionary can efficiently rectify the deviation between the visual prediction and the ground truth in scene text recognition methods. However, the independence of the dictionary on the visual features may lead to incorrect rectification of accurate visual predictions. In this paper, we propose a new dictionary language model leveraging the Scene Image-Text Matching(SITM) network, which avoids the drawbacks of the explicit dictionary language model: 1) the independence of the visual features; 2) noisy choice in candidates etc. The SITM network accomplishes this by using Image-Text Contrastive (ITC) Learning to match an image with its corresponding text among candidates in the inference stage. ITC is widely used in vision-language learning to pull the positive image-text pair closer in feature space. Inspired by ITC, the SITM network combines the visual features and the text features of all candidates to identify the candidate with the minimum distance in the feature space. Our lexicon method achieves better results(93.8\% accuracy) than the ordinary method results(92.1\% accuracy) on six mainstream benchmarks. Additionally, we integrate our method with ABINet and establish new state-of-the-art results on several benchmarks.