Adaptive aggregation of Monte Carlo augmented decomposed filters for efficient group-equivariant convolutional neural network

ArXi:2305.10110v5 Announce Type: replace Group-equivariant convolutional neural networks (G-CNN) heavily rely on parameter sharing to increase CNN's data efficiency and performance. However, the parameter-sharing strategy greatly increases the computational burden for each added parameter, which hampers its application to deep neural network models. In this paper, we address these problems by proposing a non-parameter-sharing approach for group equivariant neural networks.