Equivariant neural networks and equivarification
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2019
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| _version_ | 1866917086736941056 |
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| author | Bao, Erkao Lu, Jingcheng Song, Linqi Hart-Hodgson, Nathan Parson, William Zhou, Yanheng |
| author_facet | Bao, Erkao Lu, Jingcheng Song, Linqi Hart-Hodgson, Nathan Parson, William Zhou, Yanheng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1906_07172 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | Equivariant neural networks and equivarification Bao, Erkao Lu, Jingcheng Song, Linqi Hart-Hodgson, Nathan Parson, William Zhou, Yanheng Machine Learning Computer Vision and Pattern Recognition 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. |
| title | Equivariant neural networks and equivarification |
| topic | Machine Learning Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/1906.07172 |