Summary of Optimized Gradient Clipping For Noisy Label Learning, by Xichen Ye et al.
Optimized Gradient Clipping for Noisy Label Learning
by Xichen Ye, Yifan Wu, Weizhong Zhang, Xiaoqiang Li, Yifan Chen, Cheng Jin
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Previous research has explored ways to enhance model robustness against noisy labels by constraining the loss function’s gradient. This common practice typically involves a fixed optimal threshold for gradient clipping determined through validation data. However, this approach overlooks the dynamic distribution of gradients from both clean and noisy-labeled samples throughout training, limiting the model’s ability to adapt. To address this issue, we propose Optimized Gradient Clipping (OGC), which dynamically adjusts the clipping threshold based on the ratio of noise gradients to clean gradients after clipping. This allows OGC to control the influence of noise gradients at each training step. We provide statistical analysis to certify the noise-tolerance ability of OGC and demonstrate its effectiveness across various types of label noise, including symmetric, asymmetric, instance-dependent, and real-world noise. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Scientists have been trying to make artificial intelligence models more robust against mistakes in their training data. One way to do this is by limiting the amount of change made to the model’s predictions based on how confident it is. However, this approach has a major limitation: it doesn’t take into account how the type of mistake changes during the learning process. To solve this problem, we propose a new method called Optimized Gradient Clipping (OGC) that adjusts its limits based on the types of mistakes being made. We tested our method with different types of mistakes and found that it works well across various scenarios. |
Keywords
» Artificial intelligence » Loss function
