Learning Lie Group Symmetry Transformations with Neural Networks

Alex Gabel, Victoria Klein, Riccardo Valperga, Jeroen S. W. Lamb, Kevin Webster, Rick Quax, Efstratios Gavves

Keywords: Symmetry Detection, Lie groups, Deep Learning

TL;DR: We propose a method for parameterising one-parameter subgroups of Lie group symmetries with neural networks to detect and learn them from observations.

Abstract: The problem of detecting and quantifying the presence of symmetries in datasets is useful for model selection, generative modeling, and data analysis, amongst others. While existing methods for hard-coding transformations in neural networks require prior knowledge of the symmetries of the task at hand, this work focuses on discovering and characterising unknown symmetries present in the dataset, namely, Lie group symmetry transformations beyond the traditional ones usually considered in the field (rotation, scaling, and translation). Specifically, we consider a scenario in which a dataset has been transformed by a one-parameter subgroup of transformations with different parameter values for each data point. Our goal is to characterise the transformation group and the distribution of the parameter values, even when they aren’t small or the transformation group isn’t one of the traditional ones. The results showcase the effectiveness of the approach in both these settings.

Type Of Submission: Proceedings Track (8 pages)

Submission Number: 49