Self-Supervised Disentanglement by Leveraging Structure in Data
  Augmentations
Cian Eastwood Julius von Kügelgen Linus Ericsson Diane Bouchacourt Pascal Vincent Bernhard Schölkopf Mark Ibrahim
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 of our approach on synthetic
datasets and then present promising but limited results on ImageNet.