Leveraging Hidden Structure in Self-Supervised Learning

06/30/2021
by   Emanuele Sansone, et al.
0

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.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset