Sharpness-Aware Minimization Revisited: Weighted Sharpness as a Regularization Term
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912144012869632 |
|---|---|
| author | Yue, Yun Jiang, Jiadi Ye, Zhiling Gao, Ning Liu, Yongchao Zhang, Ke |
| author_facet | Yue, Yun Jiang, Jiadi Ye, Zhiling Gao, Ning Liu, Yongchao Zhang, Ke |
| contents | Deep Neural Networks (DNNs) generalization is known to be closely related to the flatness of minima, leading to the development of Sharpness-Aware Minimization (SAM) for seeking flatter minima and better generalization. In this paper, we revisit the loss of SAM and propose a more general method, called WSAM, by incorporating sharpness as a regularization term. We prove its generalization bound through the combination of PAC and Bayes-PAC techniques, and evaluate its performance on various public datasets. The results demonstrate that WSAM achieves improved generalization, or is at least highly competitive, compared to the vanilla optimizer, SAM and its variants. The code is available at https://github.com/intelligent-machine-learning/atorch/tree/main/atorch/optimizers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_15817 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Sharpness-Aware Minimization Revisited: Weighted Sharpness as a Regularization Term Yue, Yun Jiang, Jiadi Ye, Zhiling Gao, Ning Liu, Yongchao Zhang, Ke Machine Learning Deep Neural Networks (DNNs) generalization is known to be closely related to the flatness of minima, leading to the development of Sharpness-Aware Minimization (SAM) for seeking flatter minima and better generalization. In this paper, we revisit the loss of SAM and propose a more general method, called WSAM, by incorporating sharpness as a regularization term. We prove its generalization bound through the combination of PAC and Bayes-PAC techniques, and evaluate its performance on various public datasets. The results demonstrate that WSAM achieves improved generalization, or is at least highly competitive, compared to the vanilla optimizer, SAM and its variants. The code is available at https://github.com/intelligent-machine-learning/atorch/tree/main/atorch/optimizers. |
| title | Sharpness-Aware Minimization Revisited: Weighted Sharpness as a Regularization Term |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2305.15817 |