Clebsch-Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network
This work addresses the challenge of rotation-invariant spherical image processing for applications in fields like computer vision and astronomy, offering a more efficient method.
The paper tackles the problem of learning spherical images with rotation invariance by proposing a generalization of prior work that improves performance and simplifies implementation, achieving state-of-the-art results with a fully Fourier space approach that avoids repeated transforms.
Recent work by Cohen \emph{et al.} has achieved state-of-the-art results for learning spherical images in a rotation invariant way by using ideas from group representation theory and noncommutative harmonic analysis. In this paper we propose a generalization of this work that generally exhibits improved performace, but from an implementation point of view is actually simpler. An unusual feature of the proposed architecture is that it uses the Clebsch--Gordan transform as its only source of nonlinearity, thus avoiding repeated forward and backward Fourier transforms. The underlying ideas of the paper generalize to constructing neural networks that are invariant to the action of other compact groups.