Homomorphic Self-Supervised Learning

This paper was accepted at the workshop “Self-Supervised Learning – Theory and Practice” at NeurIPS 2022.
Many state of the art self-supervised learning approaches fundamentally rely on transformations applied to the input in order to selectively extract task-relevant information. Recently, the field of equivariant deep learning has developed to introduce structure into the feature space of deep neural networks, specifically with respect to such input transformations. In this work, we observe both theoretically and empirically, that through the lens of equivariant representations, many…Apple Machine Learning Research