Self-Supervised Detection of Perfect and Partial Input-Dependent Symmetries
Track: Proceedings
Keywords: geometric deep learning, group equivariance, symmetry discovery, self-supervised learning
TL;DR: We introduce a method to detect the level of group symmetry of each input in the dataset without the need for labels. Our framework is general enough to accommodate different families of both continuous and discrete symmetry distributions.
Abstract: Group equivariance can overly constrain models if the symmetries in the group differ from those observed in data. While common methods address this by determining the appropriate level of symmetry at the dataset level, they are limited to supervised settings and ignore scenarios in which multiple levels of symmetry co-exist in the same dataset. In this paper, we propose a method able to detect the level of symmetry of each input without the need for labels. Our framework is general enough to accommodate different families of both continuous and discrete symmetry distributions, such as arbitrary unimodal, symmetric distributions and discrete groups. We validate the effectiveness of our approach on synthetic datasets with different per-class levels of symmetries, and demonstrate practical applications such as the detection of out-of-distribution symmetries. Our code is publicly available at https://github.com/aurban0/ssl-sym.
Submission Number: 17
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