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.

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

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.