Leveraging Hidden Structure in Self-Supervised Learning

Emanuele Sansone

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arXiv · 2106.16060

Leveraging Hidden Structure in Self-Supervised Learning

Emanuele Sansone

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Abstract

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.

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Emanuele Sansone. 2021-06-30. Leveraging Hidden Structure in Self-Supervised Learning. https://arxiv.org/abs/2106.16060

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