Leveraging Hidden Structure in Self-Supervised Learning
This work addresses the challenge of improving representation learning in self-supervised settings for computer vision applications, but it appears incremental with preliminary results on a standard dataset.
The paper tackles the problem of learning structured representations from raw images using self-supervised learning, achieving higher generalization performance in downstream classification tasks on CIFAR-10 and providing more interpretable representations compared to traditional methods.
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.