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arXiv · 1906.07172

Equivariant neural networks and equivarification

Erkao Bao·Jingcheng Lu·Linqi Song·Nathan Hart-Hodgson·William Parson·Yanheng Zhou
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Abstract

Equivariant neural networks are a class of neural networks designed to preserve symmetries inherent in the data. In this paper, we introduce a general method for modifying a neural network to enforce equivariance, a process we refer to as equivarification. We further show that group convolutional neural networks (G-CNNs) arise as a special case of our framework.

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Erkao Bao, Jingcheng Lu, Linqi Song, Nathan Hart-Hodgson, William Parson, Yanheng Zhou. 2019-06-16. Equivariant neural networks and equivarification. https://arxiv.org/abs/1906.07172

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