# A General Theory of Equivariant CNNs on Homogeneous Spaces

@article{Cohen2019AGT, title={A General Theory of Equivariant CNNs on Homogeneous Spaces}, author={Taco Cohen and Mario Geiger and Maurice Weiler}, journal={ArXiv}, year={2019}, volume={abs/1811.02017} }

Group equivariant convolutional neural networks (G-CNNs) have recently emerged as a very effective model class for learning from signals in the context of known symmetries. [...] Key MethodIn addition to this classification, we use Mackey theory to show that convolutions with equivariant kernels are the most general class of equivariant maps between such fields, thus establishing G-CNNs as a universal class of equivariant networks. Expand

## 128 Citations

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