Summary of Learning with Instance-dependent Noisy Labels by Anchor Hallucination and Hard Sample Label Correction, By Po-hsuan Huang et al.
Learning with Instance-Dependent Noisy Labels by Anchor Hallucination and Hard Sample Label Correction
by Po-Hsuan Huang, Chia-Ching Lin, Chih-Fan Hsu, Ming-Ching Chang, Wei-Chao Chen
First submitted to arxiv on: 10 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 approach to Noisy-Label Learning (NLL) that addresses the limitations of traditional methods. By recognizing that even clean samples with intricate visual patterns can have high loss values, the authors develop a framework that distinguishes between easy and hard training samples. This is particularly important in datasets with Instance-Dependent Noise (IDN), where mislabeling probabilities correlate with visual appearance. The method utilizes anchors to select hard samples for label correction, improving the overall performance of semi-supervised training. Experiments on synthetic and real-world IDN datasets demonstrate the superior performance of this approach over other state-of-the-art NLL methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a big problem in machine learning – how to learn from data that’s not perfect. Right now, most approaches assume that clean data has low loss values, but what if some clean data actually looks really complicated? That can lead to mistakes! The authors come up with a new way to look at the data and separate it into easy and hard parts. This helps them correct mistakes in the hard parts, making their model better. They test this approach on fake and real data and show that it’s much better than other methods. |
Keywords
» Artificial intelligence » Machine learning » Semi supervised
