Summary of Mixed Blessing: Class-wise Embedding Guided Instance-dependent Partial Label Learning, by Fuchao Yang et al.
Mixed Blessing: Class-Wise Embedding guided Instance-Dependent Partial Label Learning
by Fuchao Yang, Jianhong Cheng, Hui Liu, Yongqiang Dong, Yuheng Jia, Junhui Hou
First submitted to arxiv on: 6 Dec 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 The proposed Instance-dependent Partial Label Learning (IDPLL) method addresses the challenges of instance-dependent noisy labels in partial label learning. The conventional PLL assumes random, instance-independent noisy labels, whereas IDPLL recognizes that practical scenarios involve noisy labels highly related to sample features. The approach creates class-wise embeddings for each sample to explore relationships between instance-dependent noisy labels and ground-truth labels. To reduce high label ambiguity, the concept of class prototypes containing global feature information is introduced. Extensive experiments on six benchmark datasets demonstrate the effectiveness of this method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers solve a problem in machine learning called partial label learning. They show that noisy labels are not random and can be very helpful for training models. However, these noisy labels can also make it hard to tell which labels are correct. To fix this, they create special “class-wise embeddings” that help understand how the noisy labels relate to each other and the true labels. This approach works well on many different datasets. |
Keywords
* Artificial intelligence * Machine learning
