Summary of Potential Energy Based Mixture Model For Noisy Label Learning, by Zijia Wang et al.
Potential Energy based Mixture Model for Noisy Label Learning
by Zijia Wang, Wenbin Yang, Zhisong Liu, Zhen Jia
First submitted to arxiv on: 2 May 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 novel Potential Energy based Mixture Model (PEMM) for noise-labels learning is a significant contribution to the field of deep neural networks. By incorporating potential energy regularization into a distance-based classifier, PEMM enables robust feature representations that preserve intrinsic data structures. This approach can be combined with existing deep learning backbones to improve noisy label tolerance. Experimental results on real-world datasets demonstrate state-of-the-art performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep learning is trying to get better at using bad labels for training. Most methods focus on the bad labels themselves, but this new idea looks at how data is structured too. It’s like potential energy in physics – it helps find patterns in data that make sense. This method combines a special classifier with regular neural networks to help them learn from noisy labels. The results show that it can do better than other methods on real-world datasets. |
Keywords
» Artificial intelligence » Deep learning » Mixture model » Regularization
