Summary of Bilateral Sharpness-aware Minimization For Flatter Minima, by Jiaxin Deng et al.
Bilateral Sharpness-Aware Minimization for Flatter Minima
by Jiaxin Deng, Junbiao Pang, Baochang Zhang, Qingming Huang
First submitted to arxiv on: 20 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 Bilateral Sharpness-Aware Minimization (BSAM) algorithm enhances generalization by minimizing a Max-Sharpness (MaxS) and incorporating a better Flatness Indicator (FI). Unlike the original Sharpness-Aware Minimization (SAM), BSAM considers both the direction of gradient ascent and the flatness in the neighborhood surrounding the current weight, denoted as Min-Sharpness (MinS). This approach leads to superior generalization performance and robustness across various tasks, including classification, transfer learning, human pose estimation, and network quantization. Theoretical analysis proves that BSAM converges to local minima, and extensive experiments demonstrate its effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a new algorithm called Bilateral Sharpness-Aware Minimization (BSAM) to help neural networks generalize better. They found that the original SAM method wasn’t perfect because it only looked at one direction when searching for the best weights. BSAM is like a more careful and thoughtful version of SAM, considering not just where the gradient points but also how flat the area is around those points. This helps find even better solutions that work well on different tasks. The team tested BSAM on many problems and found it did much better than the original SAM. |
Keywords
* Artificial intelligence * Classification * Generalization * Pose estimation * Quantization * Sam * Transfer learning
