Summary of Meat: Median-ensemble Adversarial Training For Improving Robustness and Generalization, by Zhaozhe Hu et al.
MEAT: Median-Ensemble Adversarial Training for Improving Robustness and Generalization
by Zhaozhe Hu, Jia-Li Yin, Bin Chen, Luojun Lin, Bo-Hao Chen, Ximeng Liu
First submitted to arxiv on: 20 Jun 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 Self-ensemble adversarial training methods, such as model weight averaging (WA), aim to improve model robustness against attacks like AutoAttack. However, recent research has shown that these self-ensemble defense methods still suffer from robust overfitting, leading to poor generalization performance. This issue arises when the training process becomes more focused on a specific attack, causing individual models to become overly specialized and produce anomalous weight values. To address this problem, we propose Median-Ensemble Adversarial Training (MEAT), which uses historical model weights to find the median and remove outliers. Experimental results show that MEAT achieves better robustness against AutoAttack and alleviates robust overfitting. Combining MEAT with other defense methods further enhances robust generalization and robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making machine learning models more secure by using a new method called Median-Ensemble Adversarial Training (MEAT). Right now, there are ways to make models less vulnerable to attacks, but these methods have some flaws. One problem is that they can make the model too good at handling one type of attack, which makes it bad at handling other types. MEAT tries to fix this by looking at old versions of the model and finding a middle point that’s not too special or too general. This helps the model be better at handling different types of attacks. The results show that MEAT does a good job of making models more robust and secure. |
Keywords
» Artificial intelligence » Generalization » Machine learning » Overfitting
