  Many data symmetries can be described in terms of group equivariance and the
most common way of encoding group equivariances in neural networks is by
building linear layers that are group equivariant. In this work we investigate
whether equivariance of a network implies that all layers are equivariant. On
the theoretical side we find cases where equivariance implies layerwise
equivariance, but also demonstrate that this is not the case generally.
Nevertheless, we conjecture that CNNs that are trained to be equivariant will
exhibit layerwise equivariance and explain how this conjecture is a weaker
version of the recent permutation conjecture by Entezari et al. [2022]. We
perform quantitative experiments with VGG-nets on CIFAR10 and qualitative
experiments with ResNets on ImageNet to illustrate and support our theoretical
findings. These experiments are not only of interest for understanding how
group equivariance is encoded in ReLU-networks, but they also give a new
perspective on Entezari et al.'s permutation conjecture as we find that it is
typically easier to merge a network with a group-transformed version of itself
than merging two different networks.
