Summary of Eliminating Catastrophic Overfitting Via Abnormal Adversarial Examples Regularization, by Runqi Lin et al.
Eliminating Catastrophic Overfitting Via Abnormal Adversarial Examples Regularization
by Runqi Lin, Chaojian Yu, Tongliang Liu
First submitted to arxiv on: 11 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 Single-step Adversarial Training (SSAT) method demonstrates promising results in achieving both efficiency and robustness. However, SSAT is susceptible to catastrophic overfitting (CO), which leads to a severely distorted classifier that can be exploited by multi-step adversarial attacks. The researchers identify abnormal adversarial examples (AAEs) as a key indicator of CO, which are generated during the training process despite having decreasing loss values. By analyzing the relationship between AAEs and classifier distortion, they find that the onset of CO is preceded by a slight distortion in the classifier, which accelerates its distortion and increases the variation of AAEs in a vicious circle. To mitigate this issue, the authors introduce Abnormal Adversarial Examples Regularization (AAER), a novel method that regularizes the variation of AAEs to prevent the classifier from becoming distorted. Experimental results show that AAER effectively eliminates CO while maintaining adversarial robustness with minimal additional computational overhead. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to train AI models, called Single-step Adversarial Training (SSAT). This method is good at making models both fast and secure. However, it has a problem where the model can become distorted and vulnerable to attacks. The researchers found that this distortion is caused by “abnormal” examples that are generated during training. These abnormal examples make the model worse over time. To fix this issue, they created a new method called AAER (Abnormal Adversarial Examples Regularization). This method helps prevent the model from becoming distorted and makes it more secure. They tested their method and found that it works well. |
Keywords
* Artificial intelligence * Overfitting * Regularization
