This repository is an uncleaned and preliminary PyTorch implementation of the method proposed in the paper A Probabilistic Approach to Learning the Degree of Equivariance in Steerable CNNs, accepted at ICML 2024 (see reference below). This paper presents a novel approach to create Steerable CNNs with layer-wise learnable degree of $(\mathbb{R}^n, +) \rtimes H$ equivariance, where $H\leq O(n)$ is learnable.

The implementations in this repository are based on the escnn library, which is a PyTorch library to create steerable CNNs.

Basic requirements

Python >= 3.10 torch torchvision escnn 

Requirements for reproduction of all experiments/plots

wandb plotly matplotlib seaborn sklearn pandas 

Paper accepted at ICML 2024

@article{veefkind2024probabilistic, title={A Probabilistic Approach to Learning the Degree of Equivariance in Steerable CNNs}, author={Veefkind, Lars and Cesa, Gabriele}, journal={arXiv preprint arXiv:2406.03946}, year={2024} }