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--- |
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language: en |
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tags: |
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- pythae |
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- reproducibility |
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license: apache-2.0 |
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--- |
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This model was trained with pythae. It can be downloaded or reloaded using the method `load_from_hf_hub` |
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```python |
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>>> from pythae.models import AutoModel |
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>>> model = AutoModel.load_from_hf_hub(hf_hub_path="clementchadebec/reproduced_wrapped_poincare_vae") |
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``` |
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## Reproducibility |
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This trained model reproduces the results of the official implementation of [1]. |
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| Model | Dataset | Metric | Obtained value | Reference value | |
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|:---:|:---:|:---:|:---:|:---:| |
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| PoincareVAE | MNIST | NLL | 102.06 (0.00) | 101.47 (0.01) | |
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[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. |
