This software package provides the PyTorch implementation of Partial Group Convolutional Neural Networks described in the NeurIPS 2022 paper "Learning Partial Equivariances from Data". Partial G-CNNs are able to learn layer-wise levels of partial and full equivariance to discrete, continuous groups and combinations thereof, directly from data. Partial G-CNNs retain full equivariance when beneficial, but adjust it whenever it becomes harmful. The software package also provides scripts to reproduce the results in the paper.
Access software at https://github.com/merlresearch/Partial-GCNN.