Pith. sign in

REVIEW 5 cited by

Recent Advances in Adversarial Training for Adversarial Robustness

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2102.01356 v5 pith:4V424H6M submitted 2021-02-02 cs.LG cs.AIcs.CR

Tao Bai , Jinqi Luo , Jun Zhao , Bihan Wen , Qian Wang This is my paper · ORCID
classification cs.LGcs.AIcs.CR
keywords adversarialtrainingrobustnessmodelsrecentadvancesaimsapproaches
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Adversarial training is one of the most effective approaches defending against adversarial examples for deep learning models. Unlike other defense strategies, adversarial training aims to promote the robustness of models intrinsically. During the last few years, adversarial training has been studied and discussed from various aspects. A variety of improvements and developments of adversarial training are proposed, which were, however, neglected in existing surveys. For the first time in this survey, we systematically review the recent progress on adversarial training for adversarial robustness with a novel taxonomy. Then we discuss the generalization problems in adversarial training from three perspectives. Finally, we highlight the challenges which are not fully tackled and present potential future directions.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 5 Pith papers

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

  1. ADAPT: A Pseudo-labeling Approach to Combat Concept Drift in Malware Detection

    cs.LG 2025-07 conditional novelty 5.0 of 10

    A pseudo-labeling method with class-specific adaptive thresholds, label-consistent augmentation, and mixup reduces concept-drift performance loss in malware classifiers across five datasets.

  2. Adapting Under Fire: Multi-Agent Reinforcement Learning for Adversarial Drift in Network Security

    cs.CR 2025-06 reject novelty 5.0 of 10

    The paper proposes a co-evolving red-blue reinforcement learning environment for network intrusion detection and claims the blue agent recovers up to 30% accuracy after just 2 to 3 adaptation steps with 25 to 30 sampl...

  3. SELF: Self-Extend the Context Length With Logistic Growth Function

    cs.CL 2025-05 conditional novelty 5.0 of 10

    SELF replaces Self-Extend's fixed token grouping with a logistic-growth grouping schedule, giving mixed but sometimes large gains on long-context benchmarks.

  4. Explainable AI in Genomics: Transcription Factor Binding Site Prediction with Mixture of Experts

    cs.LG 2025-07 reject novelty 4.0 of 10

    A Mixture of Experts ensemble of three CNN experts improves out-of-distribution transcription factor binding site prediction, and a shift-averaged gradient method called ShiftSmooth gives more stable motif attributions.

  5. Approach to Finding a Robust Deep Learning Model

    cs.LG 2025-05 conditional novelty 4.0 of 10

    A robustness measure based on the spread of test losses across independently trained instances, plus a pruning algorithm, selects stable small CNNs for calorimeter energy and position reconstruction.

Pith tools