Summary of Stydesty: Min-max Stylization and Destylization For Single Domain Generalization, by Songhua Liu et al.
StyDeSty: Min-Max Stylization and Destylization for Single Domain Generalization
by Songhua Liu, Xin Jin, Xingyi Yang, Jingwen Ye, Xinchao Wang
First submitted to arxiv on: 1 Jun 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 scheme, StyDeSty, tackles the challenging task of single domain generalization (single DG) by introducing a stylization module that generates novel stylized samples using the source domain and a destylization module that transfers stylized and source samples to a latent domain. This interaction between modules enables the alignment of source and pseudo domains, leading to strong generalization power. The method utilizes adversarial training between the modules and incorporates a neural architecture search strategy to determine the location of the destylization layer within the backbone network. Experimental results demonstrate that StyDeSty outperforms the state-of-the-art by up to 13.44% on classification accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating a better way for machines to learn from one domain and then be good at other, unseen domains. It’s a hard problem because we usually only have data from one place. The authors propose a new method called StyDeSty that helps the machine understand how different styles of data are related. This makes it easier for the machine to learn from one domain and apply what it learned to another domain. The authors tested their method on several datasets and showed that it works better than previous methods. |
Keywords
» Artificial intelligence » Alignment » Classification » Domain generalization » Generalization
