A Novel Convolutional Neural Network Architecture with a Continuous Symmetry

Yao Liu; Hang Shao; Bing Bai

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arXiv · 2308.01621

A Novel Convolutional Neural Network Architecture with a Continuous Symmetry

Yao Liu·Hang Shao·Bing Bai

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Abstract

This paper introduces a new Convolutional Neural Network (ConvNet) architecture inspired by a class of partial differential equations (PDEs) called quasi-linear hyperbolic systems. With comparable performance on the image classification task, it allows for the modification of the weights via a continuous group of symmetry. This is a significant shift from traditional models where the architecture and weights are essentially fixed. We wish to promote the (internal) symmetry as a new desirable property for a neural network, and to draw attention to the PDE perspective in analyzing and interpreting ConvNets in the broader Deep Learning community.

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Yao Liu, Hang Shao, Bing Bai. 2023-08-03. A Novel Convolutional Neural Network Architecture with a Continuous Symmetry. https://arxiv.org/abs/2308.01621

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