Summary of Preventing Catastrophic Overfitting in Fast Adversarial Training: a Bi-level Optimization Perspective, by Zhaoxin Wang et al.
Preventing Catastrophic Overfitting in Fast Adversarial Training: A Bi-level Optimization Perspective
by Zhaoxin Wang, Handing Wang, Cong Tian, Yaochu Jin
First submitted to arxiv on: 17 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 In this research paper, the authors propose a new Fast Adversarial Training (FAT) method called FGSM-PCO to mitigate catastrophic overfitting in bi-level optimization problems. The method generates current-stage adversarial examples from historical ones and incorporates them into the training process using an adaptive mechanism. This helps alleviate catastrophic overfitting and recover an overfitted model to effective training. The authors evaluate their algorithm across three models and three datasets, demonstrating its effectiveness compared to other FAT algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper presents a new way to make machine learning models more robust against attacks. It’s called Fast Adversarial Training (FAT) and it helps models not get stuck in bad situations. The researchers created a new version of this method called FGSM-PCO, which works better than the old one by using older mistakes to help fix current problems. They tested it with different models and data and showed that it really works! |
Keywords
» Artificial intelligence » Machine learning » Optimization » Overfitting
