Summary of Adversarially Robust Generalization Theory Via Jacobian Regularization For Deep Neural Networks, by Dongya Wu and Xin Li
Adversarially robust generalization theory via Jacobian regularization for deep neural networks
by Dongya Wu, Xin Li
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Machine Learning (stat.ML)
- 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 This paper investigates the relationship between two techniques used to obtain adversarially robust deep neural networks: Jacobian regularization and adversarial training. Researchers have developed separate methods for each, but there is a lack of theoretical foundations for Jacobian regularization. The study shows that Jacobian regularization loss can serve as an upper bound on the adversarially robust loss under certain attack types. Additionally, it establishes a robust generalization gap for Jacobian regularized risk minimizers by bounding the Rademacher complexity of both standard and Jacobian regularization function classes. Experiments on MNIST data classification demonstrate that Jacobian regularized risk minimization can improve both standard and robust generalization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make deep neural networks more secure against attacks. It compares two ways to do this: one called adversarial training, and the other called Jacobian regularization. Researchers have studied adversarial training a lot, but there isn’t much known about Jacobian regularization. The study shows that Jacobian regularization is connected to adversarial training in certain ways. It also explains how this can help make neural networks more secure. This research helps us understand both theoretically and practically how to make neural networks more robust. |
Keywords
» Artificial intelligence » Classification » Generalization » Regularization
