Clebsch–Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network
Risi Kondor · Zhen Lin · Shubhendu Trivedi

Tue Dec 4th 10:45 AM -- 12:45 PM @ Room 517 AB #130

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.

Author Information

Risi Kondor (U. Chicago)
Zhen Lin (The University of Chicago)
Shubhendu Trivedi (Brown University)

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