Homomorphic Self-Supervised Learning
Overview of Homomorphic-SSL (left) and its relation to traditional Augmentation-based SSL (right). Positive pairs extracted from the lifted dimension (θ) of a rotation equivariant network (G-conv) are equivalent to pairs extracted from the separate representations of two rotated images
In this work, we observe that many existing self-supervised learning algorithms can be both unified and generalized when seen through the lens of equivariant representations. Specifically, we introduce a general framework we call Homomorphic Self-Supervised Learning, and theoretically show how it may subsume the use of input-augmentations provided an augmentation-homomorphic feature extractor. We validate this theory experimentally for simple augmentations, demonstrate how the framework fails when representational structure is removed, and further empirically explore how the parameters of this framework relate to those of traditional augmentation-based self-supervised learning. We conclude with a discussion of the potential benefits afforded by this new perspective on self-supervised learning.
T. Anderson Keller*, Xavier Suau, and Luca Zappella
ArXiv Paper: https://arxiv.org/abs/2211.08282
Accepted at: Self-Supervised Learning workshop at NeurIPS 2022
Full paper: (under review)