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Understanding adversarial robustness against on-manifold adversarial examples
Deep neural networks (DNNs) are shown to be vulnerable to adversarial examples. A well-
trained model can be easily attacked by adding small perturbations to the original data. One …
trained model can be easily attacked by adding small perturbations to the original data. One …
Stability and Generalization of Adversarial Training for Shallow Neural Networks with Smooth Activation
Adversarial training has emerged as a popular approach for training models that are robust
to inference-time adversarial attacks. However, our theoretical understanding of why and …
to inference-time adversarial attacks. However, our theoretical understanding of why and …
Stability and generalization in free adversarial training
While adversarial training methods have significantly improved the robustness of deep
neural networks against norm-bounded adversarial perturbations, the generalization gap …
neural networks against norm-bounded adversarial perturbations, the generalization gap …