Clebsch–Gordan Nets: A Fully Fourier Space Spherical Convolutional Neural Network

Authors:
Risi Kondor U. Chicago
Zhen Lin The University of Chicago
Shubhendu Trivedi Brown University

Introduction:

Recent work by Cohen 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.

Abstract:

Recent work by Cohen 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.

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