Instructions to use clementchadebec/reproduced_beta_tc_vae with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- pythae
How to use clementchadebec/reproduced_beta_tc_vae with pythae:
from pythae.models import AutoModel model = AutoModel.load_from_hf_hub("clementchadebec/reproduced_beta_tc_vae") - Notebooks
- Google Colab
- Kaggle
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_beta_tc_vae")
Reproducibility
This trained model reproduces the results of the official implementation of [1].
| Model | Dataset | Metric | Obtained value | Reference value |
|---|---|---|---|---|
| BetaTCVAE | DSPRITES | ELBO/Modified ELBO (after 50 epochs) | 710.41/85.54 | 712.26/86.40 |
[1] Ricky TQ Chen, Xuechen Li, Roger B Grosse, and David K Duvenaud. Isolating sources of disentanglement in variational autoencoders. Advances in neural information processing systems, 31, 2018.
Inference Providers NEW
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from pythae.models import AutoModel model = AutoModel.load_from_hf_hub("clementchadebec/reproduced_beta_tc_vae")