Official PyTorch implementation of Equivariant-SSL (E-SSL).
@article{dangovski2021equivariant,
title={Equivariant Contrastive Learning},
author={Dangovski, Rumen and Jing, Li and Loh, Charlotte and Han, Seungwook and Srivastava, Akash and Cheung, Brian and Agrawal, Pulkit and Solja{\v{c}}i{\'c}, Marin},
journal={arXiv preprint arXiv:2111.00899},
year={2021}
}
The code for each dataset is self-contained. Please, inspect imagenet/, cifar10/ and photonics/ for the
corresponding datasets.
Let us know about interesting work with E-SSL and we will spread the word here.
Our work is accepted at ICLR 2022. Please, follow the project's webpage for updates.
This project is released under MIT License, which allows commercial use. See LICENSE for details.