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통찰 - Adversarial Robustness in Deep Learning
Addressing Robustness in Adversarial Training with Self-Guided Label Refinement
Label refinement in adversarial training mitigates robust overfitting by addressing noisy labels.
Reprogramming Pre-trained Deep Learning Models for Enhanced Robustness Against Adversarial Attacks
This paper introduces a novel approach called Robustness Reprogramming, which enhances the robustness of pre-trained deep learning models against adversarial attacks without modifying their original parameters, achieving this by replacing the traditional linear feature transformation with a robust non-linear pattern matching technique.
