Summary of Overcoming Data Inequality Across Domains with Semi-supervised Domain Generalization, by Jinha Park et al.
Overcoming Data Inequality across Domains with Semi-Supervised Domain Generalization
by Jinha Park, Wonguk Cho, Taesup Kim
First submitted to arxiv on: 8 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 machine learning research paper addresses a critical issue in data-driven modeling: the disparity in data availability across various sources and populations. The authors propose a novel algorithm, ProUD, to tackle Semi-Supervised Domain Generalization (SSDG), where only one domain is labeled while the rest are unlabeled. ProUD leverages domain-aware prototypes and progressive generalization via uncertainty-adaptive mixing of labeled and unlabeled domains. Experimental results on three benchmark datasets demonstrate the effectiveness of ProUD, outperforming baseline models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem in using computers to learn from data: some groups have way more data than others. This makes it hard for machines to learn fair rules that work for everyone. The authors create a new way to fix this issue by mixing different types of data together and making sure the computer learns what’s important. They test their idea on three sets of data and show it works better than other methods. |
Keywords
* Artificial intelligence * Domain generalization * Generalization * Machine learning * Semi supervised
