AFN: Adaptive Fusion Normalization via an Encoder-Decoder Framework
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866913236781105152 |
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| author | Zhou, Zikai Zhang, Shuo Wang, Ziruo Chen, Huanran |
| author_facet | Zhou, Zikai Zhang, Shuo Wang, Ziruo Chen, Huanran |
| contents | The success of deep learning is inseparable from normalization layers. Researchers have proposed various normalization functions, and each of them has both advantages and disadvantages. In response, efforts have been made to design a unified normalization function that combines all normalization procedures and mitigates their weaknesses. We also proposed a new normalization function called Adaptive Fusion Normalization. Through experiments, we demonstrate AFN outperforms the previous normalization techniques in domain generalization and image classification tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_03321 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | AFN: Adaptive Fusion Normalization via an Encoder-Decoder Framework Zhou, Zikai Zhang, Shuo Wang, Ziruo Chen, Huanran Machine Learning Computer Vision and Pattern Recognition The success of deep learning is inseparable from normalization layers. Researchers have proposed various normalization functions, and each of them has both advantages and disadvantages. In response, efforts have been made to design a unified normalization function that combines all normalization procedures and mitigates their weaknesses. We also proposed a new normalization function called Adaptive Fusion Normalization. Through experiments, we demonstrate AFN outperforms the previous normalization techniques in domain generalization and image classification tasks. |
| title | AFN: Adaptive Fusion Normalization via an Encoder-Decoder Framework |
| topic | Machine Learning Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2308.03321 |