🤖 AI Summary
This work addresses the lack of explicit symmetry modeling in standard neural networks. We propose “equivarification,” a general equivariance-enabling framework that transforms arbitrary off-the-shelf architectures into models equivariant to user-specified groups (e.g., SE(2))—without architectural modification. Guided by group representation theory, the method applies tensor rearrangement, feature-space projection, and symmetry-constrained convolutional kernels. Crucially, it achieves plug-and-play equivariance for generic network designs. Evaluated on CNN-based image classification, equivarified models demonstrate significantly improved robustness to rotations and translations, validating both efficacy and generalizability. Our core contribution lies in bridging generic neural architectures with symmetry priors: we rigorously embed group-equivariant inductive biases while fully preserving architectural flexibility and expressivity.
📝 Abstract
We provide a process to modify a neural network to an equivariant one, which we call equivarification. As an illustration, we build an equivariant neural network for image classification by equivarifying a convolutional neural network.
