  Disentangling the underlying feature attributes within an image with no prior
supervision is a challenging task. Models that can disentangle attributes well
provide greater interpretability and control. In this paper, we propose a
self-supervised framework DisCont to disentangle multiple attributes by
exploiting the structural inductive biases within images. Motivated by the
recent surge in contrastive learning paradigms, our model bridges the gap
between self-supervised contrastive learning algorithms and unsupervised
disentanglement. We evaluate the efficacy of our approach, both qualitatively
and quantitatively, on four benchmark datasets.
