New theory bounds Adversarial Rademacher Complexity for deep neural networks

Researchers have developed the first theoretical bound for Adversarial Rademacher Complexity (ARC) in deep neural networks (DNNs). This new bound addresses the challenge of generalizing DNNs to perturbed test data, a problem that has persisted despite their ability to fit perturbed training data. The approach introduces a concept of 'intermediate adversarial examples' and a compatible framework for calculating covering numbers, offering a qualitative comparison to existing Rademacher complexity bounds. Experiments indicate that the weight norm is a significant factor in the robust generalization gap observed in DNNs. AI

IMPACT Provides a theoretical framework to improve the robustness of deep neural networks against adversarial attacks.

RANK_REASON Academic paper on a theoretical advancement in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

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• Adversarial Rademacher Complexity
• arXiv
• Deep Neural Networks
• Hugging Face
• Jiancong Xiao
• Khîm
• Luo
• Rademacher Complexity
• Yin

• paper
• safety

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