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Improving adversarial robustness of deep neural networks via adaptive margin evolution

Neurocomputing (Amsterdam), 2023-09, Vol.551, p.126524, Article 126524

2023

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A3T: accuracy aware adversarial training

Machine learning, 2023-09, Vol.112 (9), p.3191-3210

2023

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ASAT: Adaptively scaled adversarial training in time series

Neurocomputing (Amsterdam), 2023-02, Vol.522, p.11-23

2023

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Ensemble adversarial training based defense against adversarial attacks for machine learning-based intrusion detection system

Neural Network World, 2023, Vol.33 (5), p.317-336

2023

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Supervised Contrastive Learning for RFF Identification with Limited Samples

IEEE internet of things journal, 2023-10, Vol.10 (19), p.1-1

2023

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A Simple Way to Make Neural Networks Robust Against Diverse Image Corruptions

Computer Vision - ECCV 2020, 2020, Vol.12348, p.53-69

2020

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Sparse Mixture Once-for-all Adversarial Training for Efficient in-situ Trade-off between Accuracy and Robustness of DNNs

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023, p.1-5

2023

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Improving non-negative Positive-Unlabeled learning for news headline classification

IEEE access, 2023-01, Vol.11, p.1-1

2023

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ISDAT: An image-semantic dual adversarial training framework for robust image classification

Pattern recognition, 2024-09, p.110968, Article 110968

2024

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Adversarial Regularization for Attention Based End-to-End Robust Speech Recognition

IEEE/ACM transactions on audio, speech, and language processing, 2019-11, Vol.27 (11), p.1826-1838

2019

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Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning

IEEE transactions on pattern analysis and machine intelligence, 2019-08, Vol.41 (8), p.1979-1993

2019

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Application of Adversarial Training in the Detection of Calcification Regions from Dental Panoramic Radiographs

IEICE transactions on fundamentals of electronics, communications and computer sciences, 2024, Article 2024SML0002

2024

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Multi-feedback Pairwise Ranking via Adversarial Training for Recommender

Chinese Journal of Electronics, 2020-07, Vol.29 (4), p.615-622

2020

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A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults

Knowledge-based systems, 2019-02, Vol.165, p.474-487

2019

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Improve individual fairness in federated learning via adversarial training

Computers & security, 2023-09, Vol.132, p.103336, Article 103336

2023

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Adversarial training for signal modulation classification based on Ulam stability theory

Digital signal processing, 2024-10, Vol.153, p.104636, Article 104636

2024

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Collaborative fault diagnosis of rotating machinery via dual adversarial guided unsupervised multi-domain adaptation network

Mechanical systems and signal processing, 2023-09, Vol.198, p.110427, Article 110427

2023

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Provable Unrestricted Adversarial Training without Compromise with Generalizability

IEEE transactions on pattern analysis and machine intelligence, 2024-05, Vol.PP, p.1-18

2024

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GRASS: generative recursive autoencoders for shape structures

ACM transactions on graphics, 2017-08, Vol.36 (4), p.1-14

2017

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Denoising the Optical Fiber Seismic Data by Using Convolutional Adversarial Network Based on Loss Balance

IEEE transactions on geoscience and remote sensing, 2021-12, Vol.59 (12), p.10544-10554

2021

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A two-stage co-adversarial perturbation to mitigate out-of-distribution generalization of large-scale graph

Expert systems with applications, 2024-12, Vol.255, p.124472, Article 124472

2024

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A survey of robust adversarial training in pattern recognition: Fundamental, theory, and methodologies

Pattern recognition, 2022-11, Vol.131, p.108889, Article 108889

2022

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Rethinking the validity of perturbation in single-step adversarial training

Pattern recognition, 2025-02, Vol.158, p.111007, Article 111007

2025

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CATIL: Customized adversarial training based on instance loss

Information sciences, 2025-01, Vol.689, p.121420, Article 121420

2025

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Distribution Bias Aware Collaborative Generative Adversarial Network for Imbalanced Deep Learning in Industrial IoT

IEEE transactions on industrial informatics, 2023-01, Vol.19 (1), p.570-580

2023

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Improving adversarial robustness of deep neural networks via adaptive margin evolution

A3T: accuracy aware adversarial training

ASAT: Adaptively scaled adversarial training in time series

Ensemble adversarial training based defense against adversarial attacks for machine learning-based intrusion detection system

Supervised Contrastive Learning for RFF Identification with Limited Samples

A Simple Way to Make Neural Networks Robust Against Diverse Image Corruptions

Sparse Mixture Once-for-all Adversarial Training for Efficient in-situ Trade-off between Accuracy and Robustness of DNNs

Improving non-negative Positive-Unlabeled learning for news headline classification

ISDAT: An image-semantic dual adversarial training framework for robust image classification

Adversarial Regularization for Attention Based End-to-End Robust Speech Recognition

Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning

Application of Adversarial Training in the Detection of Calcification Regions from Dental Panoramic Radiographs

Multi-feedback Pairwise Ranking via Adversarial Training for Recommender

A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults

Improve individual fairness in federated learning via adversarial training

Adversarial training for signal modulation classification based on Ulam stability theory

Collaborative fault diagnosis of rotating machinery via dual adversarial guided unsupervised multi-domain adaptation network

Provable Unrestricted Adversarial Training without Compromise with Generalizability

GRASS: generative recursive autoencoders for shape structures

Denoising the Optical Fiber Seismic Data by Using Convolutional Adversarial Network Based on Loss Balance

A two-stage co-adversarial perturbation to mitigate out-of-distribution generalization of large-scale graph

A survey of robust adversarial training in pattern recognition: Fundamental, theory, and methodologies

Rethinking the validity of perturbation in single-step adversarial training

CATIL: Customized adversarial training based on instance loss

Distribution Bias Aware Collaborative Generative Adversarial Network for Imbalanced Deep Learning in Industrial IoT

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