Architectural Optimization over Subgroups for Equivariant Neural Networks. (arXiv:2210.05484v1 [cs.LG])

Incorporating equivariance to symmetry groups as a constraint during neural
network training can improve performance and generalization for tasks
exhibiting those symmetries, but such symmetries are often not perfectly nor
explicitly present. This motivates algorithmically optimizing the architectural
constraints imposed by equivariance. We propose the equivariance relaxation
morphism, which preserves functionality while reparameterizing a group
equivariant layer to operate with equivariance constraints on a subgroup, as
well as the $[G]$-mixed equivariant layer, which mixes layers constrained to
different groups to …

arxiv networks neural networks optimization subgroups