Awesome
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style [NeurIPS 2021]
Official code to reproduce the results and data presented in the paper Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style.
<p align="center"> <img src="https://github.com/ysharma1126/ssl_identifiability/blob/master/problem_formulation.png?raw=true" width="300" alt="Problem Formulation" /> </p>Numerical data
To train:
> python main_mlp.py --style-change-prob 0.75 --statistical-dependence --content-dependent-style
To evaluate:
> python main_mlp.py --style-change-prob 0.75 --statistical-dependence --content-dependent-style --evaluate
Causal3DIdent Dataset
<p align="center"> <img src="https://github.com/ysharma1126/ssl_identifiability/blob/master/causal_3dident.png?raw=true" alt="Causal3DIdent dataset example images" /> </p>You can access the dataset here. The training and test datasets consists of 250000 and 25000 samples, respectively.
High-dimensional images: Causal3DIdent
To train:
> python main_3dident.py --offline-dataset OFFLINE_DATASET --apply-random-crop --apply-color-distortion
To evaluate:
> python main_3dident.py --offline-dataset OFFLINE_DATASET --apply-random-crop --apply-color-distortion --evaluate
BibTeX
@inproceedings{vonkugelgen2021self,
title={Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style},
author={von Kügelgen, Julius and Sharma, Yash and Gresele, Luigi and Brendel, Wieland and Schölkopf, Bernhard and Besserve, Michel and Locatello, Francesco},
booktitle={Advances in Neural Information Processing Systems},
year={2021}
}
Acknowledgements
This repository builds on the following codebase. If you find the dataset/code provided here to be useful, I would recommend you to also cite the following,
@article{zimmermann2021cl,
author = {
Zimmermann, Roland S. and
Sharma, Yash and
Schneider, Steffen and
Bethge, Matthias and
Brendel, Wieland
},
title = {
Contrastive Learning Inverts
the Data Generating Process
},
booktitle = {Proceedings of the 38th International Conference on Machine Learning,
{ICML} 2021, 18-24 July 2021, Virtual Event},
series = {Proceedings of Machine Learning Research},
volume = {139},
pages = {12979--12990},
publisher = {{PMLR}},
year = {2021},
url = {http://proceedings.mlr.press/v139/zimmermann21a.html},
}