A Novel Convolutional Neural Network Architecture with a Continuous Symmetry
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866914801240768512 |
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| author | Liu, Yao Shao, Hang Bai, Bing |
| author_facet | Liu, Yao Shao, Hang Bai, Bing |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_01621 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Novel Convolutional Neural Network Architecture with a Continuous Symmetry Liu, Yao Shao, Hang Bai, Bing Computer Vision and Pattern Recognition Machine Learning Neural and Evolutionary Computing 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. |
| title | A Novel Convolutional Neural Network Architecture with a Continuous Symmetry |
| topic | Computer Vision and Pattern Recognition Machine Learning Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2308.01621 |