Summary of Owmatch: Conditional Self-labeling with Consistency For Open-world Semi-supervised Learning, by Shengjie Niu et al.
OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning
by Shengjie Niu, Lifan Lin, Jian Huang, Chao Wang
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
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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 OwMatch framework combines conditional self-labeling and open-world hierarchical thresholding to tackle the challenge of unseen classes in semi-supervised learning. By revisiting methodologies from self-supervised and semi-supervised learning, this study demonstrates substantial performance enhancements across both known and unknown classes compared to previous studies. Theoretical analysis ensures the unbiasedness of the self-label assignment estimator with reliability. Code is available at https://github.com/niusj03/OwMatch. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary OwMatch is a new way to use semi-supervised learning, where some data has labels, but most doesn’t. This makes it harder because we don’t know what all the classes are. The researchers took two old ideas, self-labeling and consistency, and made them work for this problem. They called their new method OwMatch. It’s like a special filter that helps make sure things get classified correctly. The results show that OwMatch is really good at doing this, even when it doesn’t know what some of the classes are. |
Keywords
» Artificial intelligence » Self supervised » Semi supervised
