Results 11 to 20 of about 219,753 (266)
Adversarial Training for Free!
CoRR, 2019 Adversarial training, in which a network is trained on adversarial examples, is one of the few defenses against adversarial attacks that withstands strong attacks.
Davis, Larry S. +8 more
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Adversarial Training For Sketch Retrieval [PDF]
, 2016 Generative Adversarial Networks (GAN) are able to learn excellent representations for unlabelled data which can be applied to image generation and scene classification. Representations learned by GANs have not yet been applied to retrieval. In this paper,
Bharath, Anil Anthony, Creswell, Antonia
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Neural Networks, 2023 Adversarial robustness is considered as a required property of deep neural networks. In this study, we discover that adversarially trained models might have significantly different characteristics in terms of margin and smoothness, even they show similar robustness.
Hoki Kim +3 more
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Curriculum Adversarial Training [PDF]
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018 Recently, deep learning has been applied to many security-sensitive applications, such as facial authentication. The existence of adversarial examples hinders such applications. The state-of-the-art result on defense shows that adversarial training can be applied to train a robust model on MNIST against adversarial examples; but it fails to achieve a ...
Qi-Zhi Cai, Chang Liu 0021, Dawn Song
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Calibrated Adversarial Training
CoRR, 2021 ACML 2021 accepted,24 ...
Tianjin Huang +3 more
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Combining Adversaries with Anti-adversaries in Training
Proceedings of the AAAI Conference on Artificial Intelligence, 2023 Adversarial training is an effective learning technique to improve the robustness of deep neural networks. In this study, the influence of adversarial training on deep learning models in terms of fairness, robustness, and generalization is theoretically investigated under more general perturbation scope that different samples can have different ...
Xiaoling Zhou, Nan Yang, Ou Wu
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Efficient Adversarial Training With Transferable Adversarial Examples [PDF]
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 Adversarial training is an effective defense method to protect classification models against adversarial attacks. However, one limitation of this approach is that it can require orders of magnitude additional training time due to high cost of generating strong adversarial examples during training.
Haizhong Zheng +4 more
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Recent Advances in Adversarial Training for Adversarial Robustness [PDF]
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021 Adversarial training is one of the most effective approaches for deep learning models to defend against adversarial examples. Unlike other defense strategies, adversarial training aims to enhance the robustness of models intrinsically. During the past few years, adversarial training has been studied and discussed from various aspects, which deserves ...
Tao Bai +4 more
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2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 CVPR2022
Tao Li 0054 +4 more
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Self-Supervised Adversarial Training [PDF]
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020 Recent work has demonstrated that neural networks are vulnerable to adversarial examples. To escape from the predicament, many works try to harden the model in various ways, in which adversarial training is an effective way which learns robust feature representation so as to resist adversarial attacks.
Kejiang Chen +8 more
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