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Adversarial training based on the minimax formulation is necessary for obtaining adversarial robustness of trained models.
Rademacher and gaussian complexities: Risk bounds and structural results
Bartlett, P. L. and Mendelson, S · 2002
Earlier work this paper cites.
Visualizing data using t-sne
Maaten, L. v. d. and Hinton, G · 2008
Earlier work this paper cites.
Curriculum learning
Bengio, Y., Louradour, J., Collobert, R., and Weston, J · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
Earlier work this paper cites.
Principal component analysis
Abdi, H. and Williams, L. J · 2010
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A. Y · 2011
Earlier work this paper cites.
Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
Distributional smoothing by virtual adversarial examples
Miyato, T., Maeda, S., Koyama, M., Nakae, K., and Ishii, S · 2016
Earlier work this paper cites.
Zagoruyko, S. and Komodakis, N · 2016
Earlier work this paper cites.
Towards evaluating the robustness of neural networks
Carlini, N. and Wagner, D. A · 2017
Earlier work this paper cites.
Parseval networks: Improving robustness to adversarial examples
Cisse, M., Bojanowski, P., Grave, E., Dauphin, Y., and Usunier, N · 2017
Earlier work this paper cites.
Formal guarantees on the robustness of a classifier against adversarial manipulation
Hein, M. and Andriushchenko, M · 2017
Earlier work this paper cites.
Autonomous vehicle implementation predictions
Litman, T · 2017
Earlier work this paper cites.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Athalye, A., Carlini, N., and Wagner, D. A · 2018
Earlier work this paper cites.
Artificial intelligence in medicine: current trends and future possibilities
Buch, V. H., Ahmed, I., and Maruthappu, M · 2018
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Curriculum adversarial training
Cai, Q., Liu, C., and Song, D · 2018
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2018
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Ensemble adversarial training: Attacks and defenses
Tramèr, F., Kurakin, A., Papernot, N., Goodfellow, I., Boneh, D., and McDaniel, P · 2018
Cited alongside, same era.
Lipschitz-Margin training: Scalable certification of perturbation invariance for deep neural networks
Tsuzuku, Y., Sato, I., and Sugiyama, M · 2018
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Evaluating the robustness of neural networks: An extreme value theory approach
Weng, T., Zhang, H., Chen, P., Yi, J., Su, D., Gao, Y., Hsieh, C., and Daniel, L · 2018
Robustness to adversarial perturbations in learning from incomplete data
Najafi, A., Maeda, S., Koyama, M., and Miyato, T · 2019
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Adversarial robustness may be at odds with simplicity
Nakkiran, P · 2019
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Improving adversarial robustness via promoting ensemble diversity
Pang, T., Xu, K., Du, C., Chen, N., and Zhu, J · 2019
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Adversarial training for free!
Shafahi, A., Najibi, M., Ghiasi, M. A., Xu, Z., Dickerson, J., Studer, C., Davis, L. S., Taylor, G., and Goldstein, T · 2019
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A simple explanation for the existence of adversarial examples with small hamming distance
Shamir, A., Safran, I., Ronen, E., and Dunkelman, O · 2019
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Cited alongside, same era.
Provable defenses against adversarial examples via the convex outer adversarial polytope
Wong, E. and Kolter, J. Z · 2018
Cited alongside, same era.
Spatially transformed adversarial examples
Xiao, C., Zhu, J., Li, B., He, W., Liu, M., and Song, D · 2018
Cited alongside, same era.
Deep defense: Training dnns with improved adversarial robustness
Yan, Z., Guo, Y., and Zhang, C · 2018
Cited alongside, same era.
Adef: an iterative algorithm to construct adversarial deformations
Alaifari, R., Alberti, G. S., and Gauksson, T · 2019
Cited alongside, same era.
Are labels required for improving adversarial robustness?
Alayrac, J., Uesato, J., Huang, P., Fawzi, A., Stanforth, R., and Kohli, P · 2019
Cited alongside, same era.
Unlabeled data improves adversarial robustness
Carmon, Y., Raghunathan, A., Schmidt, L., Liang, P., and Duchi, J. C · 2019
Cited alongside, same era.
Robustness may be at odds with accuracy
Tsipras, D., Santurkar, S., Engstrom, L., Turner, A., and Madry, A · 2019
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On the convergence and robustness of adversarial training
Wang, Y., Ma, X., Bailey, J., Yi, J., Zhou, B., and Gu, Q · 2019
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Rademacher complexity for adversarially robust generalization
Yin, D., Ramchandran, K., and Bartlett, P. L · 2019
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Adversarial training and provable defenses: Bridging the gap
Balunovic, M. and Vechev, M · 2020
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Single-step adversarial training with dropout scheduling
B.S., V. and Babu, R. V · 2020
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Understanding and mitigating the tradeoff between robustness and accuracy
Raghunathan, A., Xie, S. M., Yang, F., Duchi, J., and Liang, P · 2020
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Overfitting in adversarially robust deep learning
Rice, L., Wong, E., and Kolter, J. Z · 2020
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Improving adversarial robustness through progressive hardening
Sitawarin, C., Chakraborty, S., and Wagner, D · 2020
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Improving adversarial robustness requires revisiting misclassified examples
Wang, Y., Zou, D., Yi, J., Bailey, J., Ma, X., and Gu, Q · 2020
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Fast is better than free: Revisiting adversarial training
Wong, E., Rice, L., and Kolter, J. Z · 2020
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Towards stable and efficient training of verifiably robust neural networks
Zhang, H., Chen, H., Xiao, C., Gowal, S., Stanforth, R., Li, B., Boning, D., and Hsieh, C.-J · 2020
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