  This work considers the problem of learning structured representations from
raw images using self-supervised learning. We propose a principled framework
based on a mutual information objective, which integrates self-supervised and
structure learning. Furthermore, we devise a post-hoc procedure to interpret
the meaning of the learnt representations. Preliminary experiments on CIFAR-10
show that the proposed framework achieves higher generalization performance in
downstream classification tasks and provides more interpretable representations
compared to the ones learnt through traditional self-supervised learning.
