Bases of Steerable Kernels for Equivariant CNNs: From 2D Rotations to the Lorentz Group

ArXi:2603.12459v1 Announce Type: new We present an alternative way of solving the steerable kernel constraint that appears in the design of steerable equivariant convolutional neural networks. We find explicit real and complex bases which are ready to use, for different symmetry groups and for feature maps of arbitrary tensor type. A major advantage of this method is that it bypasses the need to numerically or analytically compute Clebsch-Gordan coefficients and works directly with the representations of the input and output feature maps.