---
language: en
tags:
- pythae
- reproducibility
license: apache-2.0
---

This model was trained with pythae. It can be downloaded or reloaded using the method `load_from_hf_hub`
```python
>>> 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 | 101.97 (0.01) | 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.