Software & Data Downloads — Partial-GCNN

Partial Group Convolutional Neural Networks for learning layer-wise levels of partial and full equivariance to discrete, continuous groups and combinations thereof, directly from data.

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.

    •  Romero, D., Lohit, S., "Learning Partial Equivariances from Data", Advances in Neural Information Processing Systems (NeurIPS), S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh, Eds., November 2022, pp. 36466-36478.
      BibTeX TR2022-148 PDF Software Presentation
      • @inproceedings{Romero2022nov,
      • author = {Romero, David and Lohit, Suhas},
      • title = {Learning Partial Equivariances from Data},
      • booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
      • year = 2022,
      • editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh},
      • pages = {36466--36478},
      • month = nov,
      • url = {https://www.merl.com/publications/TR2022-148}
      • }

    Access software at https://github.com/merlresearch/partial_gcnn.