Summary of An Unbiased Risk Estimator For Partial Label Learning with Augmented Classes, by Jiayu Hu et al.
An Unbiased Risk Estimator for Partial Label Learning with Augmented Classes
by Jiayu Hu, Senlin Shu, Beibei Li, Tao Xiang, Zhongshi He
First submitted to arxiv on: 29 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 Partial Label Learning with Augmented Class (PLLAC) is a challenging task in weakly supervised learning, where one or more classes are not visible during training but appear at inference time. To address this issue, we propose an unbiased risk estimator for PLLAC, which estimates the distribution of augmented classes by differentiating known class distributions from unlabeled data. Our approach can be equipped with arbitrary Partial Label Learning (PLL) loss functions and provides a theoretical analysis of estimation error bounds. We also add a risk-penalty regularization term to alleviate over-fitting. Experimental results on benchmark datasets, UCI, and real-world datasets demonstrate the effectiveness of our proposed approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to recognize pictures of animals, but some animal species don’t exist yet in your training data. This is called Partial Label Learning with Augmented Class (PLLAC). Our research solves this problem by creating a way to estimate new animal species without seeing them before. We tested our approach on many datasets and it works well. |
Keywords
» Artificial intelligence » Inference » Regularization » Supervised
