Summary of How Does Distribution Matching Help Domain Generalization: An Information-theoretic Analysis, by Yuxin Dong et al.
How Does Distribution Matching Help Domain Generalization: An Information-theoretic Analysis
by Yuxin Dong, Tieliang Gong, Hong Chen, Shuangyong Song, Weizhan Zhang, Chen Li
First submitted to arxiv on: 14 Jun 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 This paper formulates domain generalization from a novel probabilistic perspective, providing robustness without overly conservative solutions. The authors reveal key insights into the roles of gradient and representation matching in promoting generalization through comprehensive information-theoretic analysis. They show that existing works focusing solely on either gradient or representation alignment are insufficient to solve the domain generalization problem. To address this issue, they introduce IDM, which simultaneously aligns inter-domain gradients and representations, achieving superior performance over baseline methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Domain generalization helps learn invariance across multiple training domains, making it easier for models to adapt to new situations. This paper tries a fresh approach by looking at domain generalization from a probabilistic point of view. They analyze what makes things generalize well and come up with some important findings. It turns out that earlier methods that only focused on one aspect (either gradients or representations) aren’t enough to solve the problem. To do better, they create a new method called IDM that combines both gradient and representation alignment. This leads to even better results compared to other ways people have tried to do domain generalization. |
Keywords
» Artificial intelligence » Alignment » Domain generalization » Generalization
