Summary of Estimating Noisy Class Posterior with Part-level Labels For Noisy Label Learning, by Rui Zhao et al.
Estimating Noisy Class Posterior with Part-level Labels for Noisy Label Learning
by Rui Zhao, Bin Shi, Jianfei Ruan, Tianze Pan, Bo Dong
First submitted to arxiv on: 8 May 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 A machine learning approach for developing consistent classifiers in noisy label learning scenarios proposes augmenting supervised information with part-level labels to improve the estimation of noisy class posteriors. Existing methods may be misled by incorrect labels, overemphasizing features that don’t reflect instance characteristics, leading to significant errors. The proposed method partitions features into distinct parts, yielding part-level labels associated with these various parts. A novel transition matrix is introduced to model the relationship between noisy and part-level labels, guiding the model to integrate information from multiple parts. This framework enables more precise estimation of noisy class posteriors, ultimately improving classification performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Noisy label learning requires consistent classifiers, which relies on estimating noisy class posteriors. Existing methods train a classification model with noisy labels but may be misled by incorrect labels. To address this issue, the paper proposes using part-level labels to guide the model’s attention to instance characteristics rather than misleading features. The method partitions features into parts, creating part-level labels that help estimate noisy class posteriors more accurately. |
Keywords
» Artificial intelligence » Attention » Classification » Machine learning » Supervised
