Summary of Relation Modeling and Distillation For Learning with Noisy Labels, by Xiaming Che et al.
Relation Modeling and Distillation for Learning with Noisy Labels
by Xiaming Che, Junlin Zhang, Zhuang Qi, Xin Qi
First submitted to arxiv on: 30 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 Relation Modeling and Distillation Network (RMDNet) tackles the problem of learning with noisy labels by modeling inter-sample relationships via self-supervised learning and employing knowledge distillation to enhance understanding of latent associations. The method consists of two main modules: relation modeling, which implements contrastive learning to learn representations that eliminate the interference of noisy tags on feature extraction, and relation-guided representation learning, which utilizes learned inter-sample relations to calibrate the representation distribution for noisy samples. RMDNet is a plug-and-play framework that can integrate multiple methods to its advantage, demonstrating superior performance compared to existing methods in experiments conducted on two datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RMDNet helps computers learn better even when the training data has mistakes. This is important because real-world data often contains errors or inaccuracies. The new method uses a combination of techniques to improve learning and make it more robust. It first learns how things are related to each other, then uses this information to correct any mistakes in the data. The result is better performance and more reliable results. |
Keywords
» Artificial intelligence » Distillation » Feature extraction » Knowledge distillation » Representation learning » Self supervised
