Summary of Stabilizing Sharpness-aware Minimization Through a Simple Renormalization Strategy, by Chengli Tan et al.
Stabilizing Sharpness-aware Minimization Through A Simple Renormalization Strategy
by Chengli Tan, Jiangshe Zhang, Junmin Liu, Yicheng Wang, Yunda Hao
First submitted to arxiv on: 14 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper explores the effectiveness of sharpness-aware minimization (SAM) in improving generalization performance. However, SAM has a drawback: it is more prone to getting stuck at saddle points, which can lead to performance degradation. To address this issue, the authors propose Stable SAM (SSAM), a simple renormalization strategy that maintains the same gradient norm for descent and ascent steps. This strategy is easy to implement and integrates well with SAM and its variants. Theoretical analysis reveals that SAM’s effectiveness is only assured in a limited learning rate regime, while SSAM extends this regime and performs better than SAM with minor modifications. Experimental results on several datasets demonstrate the improved performance of SSAM. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make machine learning algorithms work better. Right now, some algorithms called Sharpness-Aware Minimization (SAM) are doing a great job, but they have a problem: sometimes they get stuck and don’t improve anymore. The authors came up with a solution called Stable SAM that makes the algorithm more reliable and improves its performance. They used math to show how this works and tested it on some real-life data sets. |
Keywords
* Artificial intelligence * Generalization * Machine learning * Sam
