A Novel Convolutional Neural Network Architecture with a Continuous Symmetry

被引：0
|
作者：
Liu, Yao [1 ]
Shao, Hang [2 ]
Bai, Bing [1 ]
机构：
[1] Tsinghua Univ, Beijing, Peoples R China
[2] Zhejiang Future Technol Inst, Jiaxing, Peoples R China
来源：
ARTIFICIAL INTELLIGENCE, CICAI 2023, PT II | 2024年 / 14474卷
关键词：
Convolutional Neural Networks; Partial Differential Equations; Continuous Symmetry;
D O I：
10.1007/978-981-99-9119-8_28
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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 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.
引用
收藏
页码：310 / 321
页数：12
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