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We demonstrate, theoretically and empirically, that adversarial robustness can significantly benefit from semisupervised learning.
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80 million tiny images: A large data set for nonparametric object and scene recognition
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Learning multiple layers of features from tiny images
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Evasion attacks against machine learning at test time
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
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Transfer feature learning with joint distribution adaptation
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Intriguing properties of neural networks
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Regularization with stochastic transformations and perturbations for deep semi-supervised learning
M. Sajjadi, M. Javanmardi, and T. Tasdizen · 2016
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Wide residual networks
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Improving the robustness of deep neural networks via stability training
S. Zheng, Y. Song, T. Leung, and I. Goodfellow · 2016
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Improved regularization of convolutional neural networks with cutout
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Temporal ensembling for semi-supervised learning
S. Laine and T. Aila · 2017
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Sgdr: Stochastic gradient descent with warm restarts
I. Loshchilov and F. Hutter · 2017
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Automatic differentiation in pytorch, 2017
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer · 2017
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
A. Tarvainen and H. Valpola · 2017
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
A. Athalye, N. Carlini, and D. Wagner · 2018
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Wild patterns: Ten years after the rise of adversarial machine learning
B. Biggio and F. Roli · 2018
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Evaluating and understanding the robustness of adversarial logit pairing
L. Engstrom, A. Ilyas, and A. Athalye · 2018
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Analysis of classifiers’ robustness to adversarial perturbations
A. Fawzi, O. Fawzi, and P. Frossard · 2018
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J. Gilmer, L. Metz, F. Faghri, S. S. Schoenholz, M. Raghu, M. Wattenberg, and I. Goodfellow · 2018
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Unsupervised domain adaptation for semantic segmentation via class-balanced self-training
Y. Zou, Z. Yu, B. V. Kumar, and J. Wang · 2018
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Improved generalization bounds for robust learning
I. Attias, A. Kontorovich, and Y. Mansour · 2019
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Adversarial examples from computational constraints
S. Bubeck, E. Price, and I. Razenshteyn · 2019
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Certified adversarial robustness via randomized smoothing
J. M. Cohen, E. Rosenfeld, and J. Z. Kolter · 2019
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Autoaugment: Learning augmentation policies from data
E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le · 2019
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Computational limitations in robust classification and win-win results
A. Degwekar, P. Nakkiran, and V. Vaikuntanathan · 2019
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On the effectiveness of interval bound propagation for training verifiably robust models
S. Gowal, K. Dvijotham, R. Stanforth, R. Bunel, C. Qin, J. Uesato, T. Mann, and P. Kohli · 2018
Cited alongside, same era.
Cross-domain weakly-supervised object detection through progressive domain adaptation
N. Inoue, R. Furuta, T. Yamasaki, and K. Aizawa · 2018
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H. Kannan, A. Kurakin, and I. Goodfellow · 2018
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Adversarial risk bounds for binary classification via function transformation
J. Khim and P. Loh · 2018
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Second-order adversarial attack and certifiable robustness
B. Li, C. Chen, W. Wang, and L. Carin · 2018
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Towards deep learning models resistant to adversarial attacks
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu · 2018
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Virtual adversarial training: a regularization method for supervised and semi-supervised learning
T. Miyato, S. Maeda, S. Ishii, and M. Koyama · 2018
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Using pre-training can improve model robustness and uncertainty
D. Hendrycks, K. Lee, and M. Mazeika · 2019
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Certified robustness to adversarial examples with differential privacy
M. Lecuyer, V. Atlidakis, R. Geambasu, D. Hsu, and S. Jana · 2019
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VC classes are adversarially robustly learnable, but only improperly
O. Montasser, S. Hanneke, and N. Srebro · 2019
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Robustness to adversarial perturbations in learning from incomplete data
A. Najafi, S. Maeda, M. Koyama, and T. Miyato · 2019
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Adversarial training can hurt generalization
A. Raghunathan, S. M. Xie, F. Yang, J. C. Duchi, and P. Liang · 2019
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Robustness may be at odds with accuracy
D. Tsipras, S. Santurkar, L. Engstrom, A. Turner, and A. Madry · 2019
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Are labels required for improving adversarial robustness?
J. Uesato, J. Alayrac, P. Huang, R. Stanforth, A. Fawzi, and P. Kohli · 2019
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Unsupervised data augmentation
Q. Xie, Z. Dai, E. Hovy, M. Luong, and Q. V. Le · 2019
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Rademacher complexity for adversarially robust generalization
D. Yin, R. Kannan, and P. Bartlett · 2019
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Adversarially robust generalization just requires more unlabeled data
R. Zhai, T. Cai, D. He, C. Dan, K. He, J. Hopcroft, and L. Wang · 2019
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Theoretically principled trade-off between robustness and accuracy
H. Zhang, Y. Yu, J. Jiao, E. P. Xing, L. E. Ghaoui, and M. I. Jordan · 2019
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Confidence regularized self-training
Y. Zou, Z. Yu, X. Liu, B. Kumar, and J. Wang · 2019
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