  We study the generalization capacity of group convolutional neural networks.
We identify precise estimates for the VC dimensions of simple sets of group
convolutional neural networks. In particular, we find that for infinite groups
and appropriately chosen convolutional kernels, already two-parameter families
of convolutional neural networks have an infinite VC dimension, despite being
invariant to the action of an infinite group.
