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Data-dependent stability analysis of adversarial training.

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Neural Networks : the Official Journal of the International Neural Network Society
|December 7, 2024
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Summary
This summary is machine-generated.

This study introduces new generalization bounds for adversarial training in deep learning, incorporating data distribution. These bounds improve understanding of robust generalization and the impact of distribution shifts.

Keywords:
Adversarial trainingData poisoning attackGeneralization boundOn-average stability analysisStochastic gradient descent

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Area of Science:

  • Deep Learning
  • Machine Learning Theory
  • Robustness in AI

Background:

  • Stability analysis is crucial for deep learning generalization.
  • Adversarial training is a key defense against attacks.
  • Existing generalization bounds lack data distribution information.

Purpose of the Study:

  • To provide generalization bounds for adversarial training that include data distribution.
  • To analyze the impact of data distribution and adversarial budget on generalization gaps.
  • To improve the understanding of robust generalization in deep learning.

Main Methods:

  • Utilizing on-average stability and high-order approximate Lipschitz conditions.
  • Deriving generalization bounds for both convex and non-convex losses.
  • Analyzing the effects of distribution shifts and adversarial budgets.

Main Results:

  • Developed novel generalization bounds incorporating data distribution for adversarial training.
  • Bounds are comparable or superior to existing uniform stability-based bounds.
  • Demonstrated the influence of distribution shifts from data poisoning on robust generalization.

Conclusions:

  • The proposed generalization bounds offer enhanced insights into adversarial training robustness.
  • Data distribution significantly impacts generalization gaps in adversarial settings.
  • Findings are critical for developing more resilient deep learning models against various attacks.