Summary of Reduction-based Pseudo-label Generation For Instance-dependent Partial Label Learning, by Congyu Qiao et al.
Reduction-based Pseudo-label Generation for Instance-dependent Partial Label Learning
by Congyu Qiao, Ning Xu, Yihao Hu, Xin Geng
First submitted to arxiv on: 28 Oct 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 In this paper, researchers propose a new approach to instance-dependent partial label learning (ID-PLL), which tackles the issue of overfitting on incorrect candidate labels. The previous methods relied on the training model itself to refine supervision information, but these approaches neglect the vulnerability of the training model to poor supervision due to incorrect candidate labels. To address this limitation, the authors introduce reduction-based pseudo-labels that are generated by aggregating the outputs of a multi-branch auxiliary model trained in label subspaces excluding certain labels. This ensures that each branch avoids being influenced by excluded labels, allowing for more accurate pseudo-labels. Theoretical analysis shows that these reduction-based pseudo-labels have greater consistency with the Bayes optimal classifier compared to traditional pseudo-labels. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is all about fixing a problem in machine learning called instance-dependent partial label learning (ID-PLL). ID-PLL means you’re trying to learn from some labeled training data, but not all of it. The tricky part is that some of the labels are wrong, and this can mess up your whole model. The researchers came up with an idea to make better “fake” labels, called pseudo-labels, by combining the predictions of many smaller models trained on specific parts of the data. This helps get rid of the bad influence from those incorrect labels, making it easier to learn something good. |
Keywords
» Artificial intelligence » Machine learning » Overfitting
