Summary of Layer-aware Analysis Of Catastrophic Overfitting: Revealing the Pseudo-robust Shortcut Dependency, by Runqi Lin et al.
Layer-Aware Analysis of Catastrophic Overfitting: Revealing the Pseudo-Robust Shortcut Dependency
by Runqi Lin, Chaojian Yu, Bo Han, Hang Su, Tongliang Liu
First submitted to arxiv on: 25 May 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 paper investigates catastrophic overfitting (CO) in single-step adversarial training (AT) of deep neural networks (DNNs). The researchers find that during CO, earlier layers are more susceptible to distortion, while latter layers show relative insensitivity. This is attributed to the formation of pseudo-robust shortcuts, which can defend against single-step attacks but bypass genuine robust learning, leading to distorted decision boundaries. To mitigate CO, the authors propose Layer-Aware Adversarial Weight Perturbation (LAP), which prevents the generation of pseudo-robust shortcuts and enhances robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Catastrophic overfitting is a problem in training deep neural networks that makes them vulnerable to attacks. Researchers have found out why this happens and how to fix it. They discovered that some layers in the network are more affected than others, and that’s because of “shortcuts” that form during training. These shortcuts make the network good at defending against one type of attack but bad at learning from real data. To solve this problem, they came up with a new method called LAP (Layer-Aware Adversarial Weight Perturbation). This method helps prevent these shortcuts from forming and makes the network more robust. |
Keywords
» Artificial intelligence » Overfitting
