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This model was trained with pythae. It can be downloaded or reloaded using the method load_from_hf_hub

>>> from pythae.models import AutoModel
>>> model = AutoModel.load_from_hf_hub(hf_hub_path="clementchadebec/reproduced_wrapped_poincare_vae")

Reproducibility

This trained model reproduces the results of the official implementation of [1].

Model Dataset Metric Obtained value Reference value
PoincareVAE MNIST NLL (500 IS) 101.66 (0.00) 101.47 (0.01)

[1] Mathieu, E., Le Lan, C., Maddison, C. J., Tomioka, R., & Teh, Y. W. (2019). Continuous hierarchical representations with poincaré variational auto-encoders. Advances in neural information processing systems, 32.

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