
### Downloading this model from the Hub
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_aae")
```
## Reproducibility
This trained model reproduces the results of Table 1 in [1].

| Model | Dataset | Metric | Obtained value | Reference value |
|:---:|:---:|:---:|:---:|:---:|
| AAE | CELEBA 64 | FID | 43.3 | 42 |

[1] Tolstikhin, O Bousquet, S Gelly, and B Schölkopf. Wasserstein auto-encoders. In 6th International Conference on Learning Representations (ICLR 2018), 2018.
{"language": "en", "license": "apache-2.0", "tags": ["pythae", "reproducibility"]}
pythae reproducibility en license:apache-2.0 region:us
TAGS
#pythae #reproducibility #en #license-apache-2.0 #region-us 

### Downloading this model from the Hub


This model was trained with pythae. It can be downloaded or reloaded using the method 'load\_from\_hf\_hub'


Reproducibility
---------------


This trained model reproduces the results of Table 1 in [1].



[1] Tolstikhin, O Bousquet, S Gelly, and B Schölkopf. Wasserstein auto-encoders. In 6th International Conference on Learning Representations (ICLR 2018), 2018.



### Downloading this model from the Hub


This model was trained with pythae. It can be downloaded or reloaded using the method 'load\_from\_hf\_hub'


Reproducibility
---------------


This trained model reproduces the results of Table 1 in [1].



[1] Tolstikhin, O Bousquet, S Gelly, and B Schölkopf. Wasserstein auto-encoders. In 6th International Conference on Learning Representations (ICLR 2018), 2018.
TAGS
#pythae #reproducibility #en #license-apache-2.0 #region-us 
 ### Downloading this model from the Hub


This model was trained with pythae. It can be downloaded or reloaded using the method 'load\_from\_hf\_hub'


Reproducibility
---------------


This trained model reproduces the results of Table 1 in [1].



[1] Tolstikhin, O Bousquet, S Gelly, and B Schölkopf. Wasserstein auto-encoders. In 6th International Conference on Learning Representations (ICLR 2018), 2018.
passage: TAGS
#pythae #reproducibility #en #license-apache-2.0 #region-us 
### Downloading this model from the Hub


This model was trained with pythae. It can be downloaded or reloaded using the method 'load\_from\_hf\_hub'


Reproducibility
---------------


This trained model reproduces the results of Table 1 in [1].



[1] Tolstikhin, O Bousquet, S Gelly, and B Schölkopf. Wasserstein auto-encoders. In 6th International Conference on Learning Representations (ICLR 2018), 2018.