General Nonlinearities in SO(2)-Equivariant CNNsDownload PDF

21 May 2021, 20:51 (edited 26 Oct 2021)NeurIPS 2021 PosterReaders: Everyone
  • Keywords: deep learning, equivariance, steerable CNNs, group convolution, harmonic distortion analysis
  • TL;DR: We improve the ability of using (more) general nonlinerities in SO(2)-equivariant steerable networks.
  • Abstract: Invariance under symmetry is an important problem in machine learning. Our paper looks specifically at equivariant neural networks where transformations of inputs yield homomorphic transformations of outputs. Here, steerable CNNs have emerged as the standard solution. An inherent problem of steerable representations is that general nonlinear layers break equivariance, thus restricting architectural choices. Our paper applies harmonic distortion analysis to illuminate the effect of nonlinearities on Fourier representations of SO(2). We develop a novel FFT-based algorithm for computing representations of non-linearly transformed activations while maintaining band-limitation. It yields exact equivariance for polynomial (approximations of) nonlinearities, as well as approximate solutions with tunable accuracy for general functions. We apply the approach to build a fully E(3)-equivariant network for sampled 3D surface data. In experiments with 2D and 3D data, we obtain results that compare favorably to the state-of-the-art in terms of accuracy while permitting continuous symmetry and exact equivariance.
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  • Code: https://github.com/da-fr
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