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CAT: Customized Adversarial Training for Improved Robustness

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arxiv 2002.06789 v1 pith:ECAMY25V submitted 2020-02-17 cs.LG stat.ML

Minhao Cheng , Qi Lei , Pin-Yu Chen , Inderjit Dhillon , Cho-Jui Hsieh This is my paper · ORCID
classification cs.LGstat.ML
keywords trainingadversarialalgorithmcleancustomizedmethodsrobustnessaccuracy
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Adversarial training has become one of the most effective methods for improving robustness of neural networks. However, it often suffers from poor generalization on both clean and perturbed data. In this paper, we propose a new algorithm, named Customized Adversarial Training (CAT), which adaptively customizes the perturbation level and the corresponding label for each training sample in adversarial training. We show that the proposed algorithm achieves better clean and robust accuracy than previous adversarial training methods through extensive experiments.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Adversarial Training in Low-Label Regimes with Margin-Based Interpolation

    cs.LG 2024-11 conditional novelty 5.0 of 10

    Margin-controlled interpolation between clean and PGD examples, combined with a curriculum-style epsilon schedule, improves both clean accuracy and robust accuracy of semi-supervised adversarial training in low-label regimes.

  2. A Survey of Secure Semantic Communications

    cs.CR 2025-01 conditional novelty 3.0 of 10

    A comprehensive survey of security and privacy challenges in semantic communication, categorized by the SemCom life cycle and paired with available defense technologies.

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