  Group Equivariant Convolutions (GConvs) enable convolutional neural networks
to be equivariant to various transformation groups, but at an additional
parameter and compute cost. We investigate the filter parameters learned by
GConvs and find certain conditions under which they become highly redundant. We
show that GConvs can be efficiently decomposed into depthwise separable
convolutions while preserving equivariance properties and demonstrate improved
performance and data efficiency on two datasets. All code is publicly available
at github.com/Attila94/SepGrouPy.
