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Adversarial training (AT) is currently one of the most successful methods to obtain the adversarial robustness of deep neural networks.
Learning multiple layers of features from tiny images
Krizhevsky, A.; et al. 2009 · 2009
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Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples
Gowal, S.; Qin, C.; Uesato, J.; Mann, T.; and Kohli, P. 2020 · 2010
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Intriguing properties of neural networks
Szegedy, C.; Zaremba, W.; Sutskever, I.; Bruna, J.; Erhan, D.; Goodfellow, I.; and Fergus, R. 2014 · 2014
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Deepdriving: Learning affordance for direct perception in autonomous driving
Chen, C.; Seff, A.; Kornhauser, A.; and Xiao, J. 2015 · 2015
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Fast r-cnn
Girshick, R. 2015 · 2015
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Explaining and harnessing adversarial examples
Goodfellow, I. J.; Shlens, J.; and Szegedy, C. 2015 · 2015
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Distilling the knowledge in a neural network
Hinton, G.; Vinyals, O.; and Dean, J. 2015 · 2015
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Deep speech 2: End-to-end speech recognition in english and mandarin
Amodei, D.; Ananthanarayanan, S.; Anubhai, R.; Bai, J.; Battenberg, E.; Case, C.; Casper, J.; Catanzaro, B.; Cheng, Q.; Chen, G.; et al. 2016 · 2016
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Adversarial examples in the physical world
Kurakin, A.; Goodfellow, I.; and Bengio, S. 2016 · 2016
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Wide residual networks
Zagoruyko, S.; and Komodakis, N. 2016 · 2016
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Towards evaluating the robustness of neural networks
Carlini, N.; and Wagner, D. 2017 · 2017
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Improved regularization of convolutional neural networks with cutout
DeVries, T.; and Taylor, G. W. 2017 · 2017
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On calibration of modern neural networks
Guo, C.; Pleiss, G.; Sun, Y.; and Weinberger, K. Q. 2017 · 2017
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Athalye, A.; Carlini, N.; and Wagner, D. 2018 · 2018
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Unsupervised representation learning by predicting image rotations
Gidaris, S.; Singh, P.; and Komodakis, N. 2018 · 2018
Cited alongside, same era.
Countering adversarial images using input transformations
Guo, C.; Rana, M.; Cisse, M.; and Van Der Maaten, L. 2018 · 2018
Cited alongside, same era.
A simple unified framework for detecting out-of-distribution samples and adversarial attacks
Lee, K.; Lee, K.; Lee, H.; and Shin, J. 2018 · 2018
Cited alongside, same era.
Defense against adversarial attacks using high-level representation guided denoiser
Liao, F.; Liang, M.; Dong, Y.; Pang, T.; Hu, X.; and Zhu, J. 2018 · 2018
Cited alongside, same era.
Characterizing adversarial subspaces using local intrinsic dimensionality
Ma, X.; Li, B.; Wang, Y.; Erfani, S. M.; Wijewickrema, S.; Schoenebeck, G.; Song, D.; Houle, M. E.; and Bailey, J. 2018 · 2018
Cited alongside, same era.
Towards Deep Learning Models Resistant to Adversarial Attacks
Theoretically Principled Trade-off between Robustness and Accuracy
Zhang, H.; Yu, Y.; Jiao, J.; Xing, E.; Ghaoui, L. E.; and Jordan, M. 2019 · 2019
Later among the works it cites.
Augmix: A simple data processing method to improve robustness and uncertainty
Hendrycks, D.; Mu, N.; Cubuk, E. D.; Zoph, B.; Gilmer, J.; and Lakshminarayanan, B. 2020 · 2020
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Consistency Regularization for Certified Robustness of Smoothed Classifiers
Jeong, J.; and Shin, J. 2020 · 2020
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Adversarial Self-Supervised Contrastive Learning
Kim, M.; Tack, J.; and Hwang, S. J. 2020 · 2020
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Adversarial robustness against the union of multiple perturbation models
Maini, P.; Wong, E.; and Kolter, Z. 2020 · 2020
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Overfitting in adversarially robust deep learning
Rice, L.; Wong, E.; and Kolter, Z. 2020 · 2020
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Madry, A.; Makelov, A.; Schmidt, L.; Tsipras, D.; and Vladu, A. 2018 · 2018
Cited alongside, same era.
Adversarially robust generalization requires more data
Schmidt, L.; Santurkar, S.; Tsipras, D.; Talwar, K.; and Madry, A. 2018 · 2018
Cited alongside, same era.
Certified adversarial robustness via randomized smoothing
Cohen, J.; Rosenfeld, E.; and Kolter, Z. 2019 · 2019
Cited alongside, same era.
Autoaugment: Learning augmentation strategies from data
Cubuk, E. D.; Zoph, B.; Mane, D.; Vasudevan, V.; and Le, Q. V. 2019 · 2019
Cited alongside, same era.
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
Hendrycks, D.; and Dietterich, T. 2019 · 2019
Cited alongside, same era.
Adversarial camera stickers: A physical camera-based attack on deep learning systems
Li, J.; Schmidt, F.; and Kolter, Z. 2019 · 2019
Cited alongside, same era.
Adversarial robustness through local linearization
Qin, C.; Martens, J.; Gowal, S.; Krishnan, D.; Dvijotham, K.; Fawzi, A.; De, S.; Stanforth, R.; and Kohli, P. 2019 · 2019
Cited alongside, same era.
Later among the works it cites.
Fixmatch: Simplifying semi-supervised learning with consistency and confidence
Sohn, K.; Berthelot, D.; Li, C.-L.; Zhang, Z.; Carlini, N.; Cubuk, E. D.; Kurakin, A.; Zhang, H.; and Raffel, C. 2020 · 2020
Later among the works it cites.
Improving Adversarial Robustness Requires Revisiting Misclassified Examples
Wang, Y.; Zou, D.; Yi, J.; Bailey, J.; Ma, X.; and Gu, Q. 2020 · 2020
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Adversarial Weight Perturbation Helps Robust Generalization
Wu, D.; Xia, S.-T.; and Wang, Y. 2020 · 2020
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Unsupervised data augmentation for consistency training
Xie, Q.; Dai, Z.; Hovy, E.; Luong, M.-T.; and Le, Q. V. 2020 · 2020
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A Closer Look at Accuracy vs. Robustness
Yang, Y.-Y.; Rashtchian, C.; Zhang, H.; Salakhutdinov, R.; and Chaudhuri, K. 2020 · 2020
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Consistency regularization for generative adversarial networks
Zhang, H.; Zhang, Z.; Odena, A.; and Lee, H. 2020 · 2020
Later among the works it cites.
Robust Overfitting may be mitigated by properly learned smoothening
Chen, T.; Zhang, Z.; Liu, S.; Chang, S.; and Wang, Z. 2021 · 2021
Closest in time.
Bag of Tricks for Adversarial Training
Pang, T.; Yang, X.; Dong, Y.; Su, H.; and Zhu, J. 2021 · 2021
Closest in time.