Summary of Enhancing Sharpness-aware Minimization by Learning Perturbation Radius, By Xuehao Wang et al.
Enhancing Sharpness-Aware Minimization by Learning Perturbation Radius
by Xuehao Wang, Weisen Jiang, Shuai Fu, Yu Zhang
First submitted to arxiv on: 15 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed LEarning the perTurbation radiuS (LETS) framework learns the perturbation radius for sharpness-aware minimization algorithms, aiming to improve model generalization by searching for flat minima in the loss landscape. This is achieved through a bilevel optimization approach, where the upper-level problem minimizes the squared generalization gap between training and validation losses, while the lower-level problem optimizes the SAM update using a computed perturbation radius. The LETS method can be combined with any variant of SAM, and experimental results on various architectures and benchmark datasets in computer vision and natural language processing demonstrate its effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The proposed framework, LEarning the perTurbation radiuS (LETS), helps improve model generalization by finding a good perturbation radius for sharpness-aware minimization algorithms. It does this by using a special way of combining two problems: one that finds the right perturbation radius and another that uses that radius to make the algorithm better. The results show that this approach works well on different computer vision and natural language processing tasks. |
Keywords
» Artificial intelligence » Generalization » Natural language processing » Optimization » Sam
