Summary of Meta Curvature-aware Minimization For Domain Generalization, by Ziyang Chen et al.
Meta Curvature-Aware Minimization for Domain Generalization
by Ziyang Chen, Yiwen Ye, Feilong Tang, Yongsheng Pan, Yong Xia
First submitted to arxiv on: 16 Dec 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 paper addresses the problem of domain generalization (DG), where models trained on one set of data need to perform well on unseen domains. Recent approaches like Sharpness-Aware Minimization (SAM) have shown promise, but their limitations hinder further improvements in model generalization. To overcome this, the authors propose a novel algorithm called Meta Curvature-Aware Minimization (MeCAM), which combines regular training loss with surrogate gaps from SAM and meta-learning to minimize curvature around local minima. Theoretical analysis shows MeCAM’s superiority over existing DG methods, demonstrated through extensive experiments on five benchmark datasets: PACS, VLCS, OfficeHome, TerraIncognita, and DomainNet. The authors also provide code on GitHub. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about helping models learn to work well in new situations they’ve never seen before. Right now, some methods like Sharpness-Aware Minimization (SAM) are good at this, but there’s still room for improvement. To get better results, the authors came up with a new way of training models called Meta Curvature-Aware Minimization (MeCAM). It combines three things: regular learning, SAM, and a way to learn from experience. This helps the model find a good spot to stop learning, rather than just stopping randomly. The authors tested their method on five different sets of data and showed that it works better than other methods. |
Keywords
» Artificial intelligence » Domain generalization » Generalization » Meta learning » Sam
