Steerable Convolution

Steerable convolutions are a class of convolutional neural network (CNN) architectures designed to incorporate geometric symmetries, improving model efficiency and robustness to transformations like rotations and reflections. Current research focuses on developing more efficient algorithms, such as adaptive sampling techniques, and extending steerable convolutions to various group symmetries (e.g., SO(3) for 3D rotations) within different model architectures, including transformers. This work is significant because it enhances the performance and generalizability of CNNs across diverse applications, particularly in areas like 3D medical image analysis, robotics, and physics simulations, where data often exhibits inherent symmetries.