Summary of Improving Accuracy-robustness Trade-off Via Pixel Reweighted Adversarial Training, by Jiacheng Zhang et al.
Improving Accuracy-robustness Trade-off via Pixel Reweighted Adversarial Training
by Jiacheng Zhang, Feng Liu, Dawei Zhou, Jingfeng Zhang, Tongliang Liu
First submitted to arxiv on: 2 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 Medium Difficulty summary: This paper proposes Pixel-reweighted AdveRsarial Training (PART), a new framework that optimizes the adversarial training process by allocating distinct weights to different pixel regions. The authors discover that not all pixels contribute equally to the accuracy on adversarial examples and natural images, leading them to design PART, which partially reduces the perturbation budget for less influential pixels. This approach is motivated by the idea of identifying important pixel regions using class activation mapping (CAM) methods and then generating pixel-reweighted adversarial examples. The authors demonstrate that PART achieves a notable improvement in accuracy without compromising robustness on CIFAR-10, SVHN, and TinyImagenet-200. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper is about making computer models more reliable by adjusting how they’re trained. Right now, models are trained using fake images with small changes to make them tricky for the model to get right. But, researchers found that not all parts of an image are equally important for this training process. They developed a new way to focus on the most important parts and ignore the less important ones. This approach helps models become more accurate without sacrificing their ability to handle unexpected images. The results show that this new method works well on three different datasets. |
