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The problem of adversarial examples has shown that modern Neural Network (NN) models could be rather fragile.
Orthogonal Deep Neural Networks
Jia, K., Li, S., Wen, Y., Liu, T., and Tao, D · 1905
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Understanding the difficulty of training deep feedforward neural networks
Glorot, X. and Bengio, Y · 2010
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Deep Sparse Rectifier Neural Networks
Glorot, X., Bordes, A., and Bengio, Y · 2011
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ImageNet Classification with Deep Convolutional Neural Networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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Robustness and generalization
Xu, H. and Mannor, S · 2012
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Distance Preserving Embeddings for General n-Dimensional Manifolds
Verma, N · 2013
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Understanding Machine Learning: From Theory to Algorithms
Shalev-Shwartz, S. and Ben-David, S · 2014
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., and Sutskever, I · 2014
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Explaining and Harnessing Adversarial Examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2015
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Deeply-Supervised Nets
Lee, C.-y., Xie, S., and Gallagher, P. W · 2015
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A unified gradient regularization family for adversarial examples
Lyu, C., Huang, K., and Liang, H. N · 2015
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Identity Mappings in Deep Residual Networks
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Wide Residual Networks
Zagoruyko, S. and Komodakis, N · 2016
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Understanding deep learning requires rethinking generalization
Zhang, C., Bengio, S., Hardt, M., Recht, B., and Vinyals, O · 2016
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Spectrally-normalized margin bounds for neural networks
Bartlett, P., Foster, D. J., and Telgarsky, M · 2017
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On the effect of pooling on the geometry of representations
Bécigneul, G · 2017
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Adversarial Machine Learning at Scale
Kurakin, A., Goodfellow, I. J., and Bengio, S · 2017
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Automatic differentiation in prose
Pfeiffer, F. W · 2017
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Robust Large Margin Deep Neural Networks
Sokolic, J., Giryes, R., Sapiro, G., and Rodrigues, M. R. D · 2017
Cited alongside, same era.
Ensemble adversarial training: Attacks and defenses
Tramèr, F., Kurakin, A., Papernot, N., Goodfellow, I., Boneh, D., and McDaniel, P · 2017
Cited alongside, same era.
The Singular Values of Convolutional Layers
Sedghi, H., Gupta, V., and Long, P. M · 2018
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Certifying Some Distributional Robustness with Principled Adversarial Training
Sinha, A., Namkoong, H., and Duchi, J · 2018
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Is Robustness the Cost of Accuracy ? – A Comprehensive Study on the Robustness of
Su, D., Zhang, H., Chen, H., Yi, J., and Aug, C. V · 2018
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Lipschitz-Margin Training : Scalable Certification of Perturbation Invariance for Deep Neural Networks
Tsuzuku, Y., Sato, I., and Sugiyama, M · 2018
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Rademacher Complexity for Adversarially Robust
Yin, D. and Bartlett, P · 2018
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Fixup Initialization: Residual Learning Without Normalization
Zhang, H., Dauphin, Y. N., and Ma, T · 2018
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Attias, I., Kontorovich, A., and Mansour, Y · 2018
Cited alongside, same era.
PAC-learning in the presence of evasion adversaries
Cullina, D., Bhagoji, A. N., and Mittal, P · 2018
Cited alongside, same era.
Analysis of classifiers’ robustness to adversarial perturbations
Fawzi, A., Fawzi, O., and Frossard, P · 2018
Cited alongside, same era.
Tiny imagenet, 2018
ImageNet, T · 2018
Cited alongside, same era.
Kannan, H., Kurakin, A., and Goodfellow, I · 2018
Cited alongside, same era.
Adversarial Risk Bounds for Binary Classification via Function Transformation
Khim, J. and Loh, P.-L · 2018
Cited alongside, same era.
Optimal Transport Classifier: Defending Against Adversarial Attacks by Regularized Deep Embedding
Li, Y., Min, M. R., Yu, W., Hsieh, C.-J., Lee, T. C. M., and Kruus, E · 2018
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Adversarial examples are not bugs, they are features
Ilyas, A., Santurkar, S., Tsipras, D., Engstrom, L., Tran, B., and Madry, A · 2019
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Robustness via curvature regularization, and vice versa
Moosavi-Dezfooli, Mohsen, S., Fawzi, A., Uesato, J., and Frossard, P · 2019
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Uniform convergence may be unable to explain generalization in deep learning
Nagarajan, V. and Kolter, J. Z · 2019
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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
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Robustness May Be at Odds with Accuracy
Tsipras, D., Santurkar, S., Engstrom, L., Turner, A., and Ma̧dry, A · 2019
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Bilateral adversarial training: Towards fast training of more robust models against adversarial attacks
Wang, J. and Zhang, H · 2019
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Feature denoising for improving adversarial robustness
Xie, C., Wu, Y., Maaten, L. v. d., Yuille, A. L., and He, K · 2019
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Theoretically Principled Trade-off between Robustness and Accuracy
Zhang, H., Yu, Y., Jiao, J., Xing, E. P., Ghaoui, L. E., and Jordan, M. I · 2019
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