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.