Before revision: 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. A wide variety of equivariant layers has been proposed for signals on 2D and 3D Euclidean space , graphs, and the sphere , and it has become difficult to see how all of these methods are related, and how they may be generalized.
After revision: Group equivariant convolutional neural networks (G-CNNs) on homogeneous spaces such as Euclidean space and 3D Euclidean space , graphs, and the sphere , and it has become difficult to see how all of these methods are related, and how they may be generalized.