Summary of Gbrip: Granular Ball Representation For Imbalanced Partial Label Learning, by Jintao Huang et al.
GBRIP: Granular Ball Representation for Imbalanced Partial Label Learning
by Jintao Huang, Yiu-ming Cheung, Chi-man Vong, Wenbin Qian
First submitted to arxiv on: 19 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 proposes a novel framework called Granular Ball Representation for Imbalanced Partial Label Learning (GBRIP) to address the limitations of existing weakly supervised multi-classification methods in dealing with class imbalance. GBRIP uses coarse-grained granular ball representation and multi-center loss to construct a feature space that captures the distribution within each class, mitigating the impact of confusing features. The method includes label disambiguation and estimating imbalance distributions. Experimental results on standard benchmarks show that GBRIP outperforms existing state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem called partial label learning, which is hard because some labels are missing or wrong. It’s like trying to learn to recognize animals without knowing all the animal names! The method uses special math to group similar features together and make it easier to learn from the data. This helps the model avoid mistakes caused by confusing features. The results show that this method works better than other methods for solving this problem. |
Keywords
» Artificial intelligence » Classification » Supervised
