readme: ---
  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_svae")
  ```
  
  ## Reproducibility
  This trained model reproduces the results of Table 1 in [1].
  
  | Model | Dataset | Metric | Obtained value | Reference value |
  |:---:|:---:|:---:|:---:|:---:|
  | SVAE | Dyn. Binarized MNIST | NLL (500 IS) | 93.13 (0.01) | 93.16 (0.31) |
  
  [1] Tim R Davidson, Luca Falorsi, Nicola De Cao, Thomas Kipf, and Jakub M Tomczak. Hyperspherical variational auto-encoders. In 34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018, pages 856–865. Association For Uncertainty in Artificial Intelligence (AUAI), 2018.