@inproceedings{
eastwood2023selfsupervised,
title={Self-Supervised Disentanglement by Leveraging Structure in Data Augmentations},
author={Cian Eastwood and Julius von K{\"u}gelgen and Linus Ericsson and Diane Bouchacourt and Pascal Vincent and Mark Ibrahim and Bernhard Sch{\"o}lkopf},
booktitle={Causal Representation Learning Workshop at NeurIPS 2023},
year={2023},
url={https://openreview.net/forum?id=JoISqbH8vl}
}
Self-supervised representation learning often uses data augmentations to induce some invariance to "style" attributes of the data. However, with downstream tasks generally unknown at training time, it is difficult to deduce a priori which attributes of the data are indeed "style" and can be safely discarded. To address this, we introduce a more principled approach that seeks to disentangle style features rather than discard them. The key idea is to add multiple style embedding spaces where: (i) each is invariant to all-but-one augmentation; and (ii) joint entropy is maximized. We formalize our structured data-augmentation procedure from a causal latent-variable-model perspective, and prove identifiability of both content and (multiple blocks of) style variables. We empirically demonstrate the benefits our approach on synthetic datasets and then present promising but limited results on ImageNet.
Self-Supervised Disentanglement by Leveraging Structure in Data Augmentations
Cian Eastwood Julius von Kügelgen Linus Ericsson Diane Bouchacourt Pascal Vincent Mark Ibrahim Bernhard Schölkopf