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_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.