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We study two important concepts in adversarial deep learning---adversarial training and generative adversarial network (GAN).
The unreasonable effectiveness of data
A. Halevy, P. Norvig, and F. Pereira · 2009
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2013
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2014
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Conditional generative adversarial nets
M. Mirza and S. Osindero · 2014
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Deep generative image models using a laplacian pyramid of adversarial networks
E. L. Denton, S. Chintala, R. Fergus, et al · 2015
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Deep multi-scale video prediction beyond mean square error
M. Mathieu, C. Couprie, and Y. LeCun · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
A. Radford, L. Metz, and S. Chintala · 2015
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Distillation as a defense to adversarial perturbations against deep neural networks
N. Papernot, P. McDaniel, X. Wu, S. Jha, and A. Swami · 2016
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Towards principled methods for training generative adversarial networks
M. Arjovsky and L. Bottou · 2017
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M. Arjovsky, S. Chintala, and L. Bottou · 2017
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Began: Boundary equilibrium generative adversarial networks
D. Berthelot, T. Schumm, and L. Metz · 2017
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Towards evaluating the robustness of neural networks
N. Carlini and D. Wagner · 2017
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Parseval networks: Improving robustness to adversarial examples
M. Cisse, P. Bojanowski, E. Grave, Y. Dauphin, and N. Usunier · 2017
Cited alongside, same era.
Improved training of wasserstein gans
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville · 2017
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Improved training of wasserstein gans
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville · 2017
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Stacked generative adversarial networks
X. Huang, Y. Li, O. Poursaeed, J. Hopcroft, and S. Belongie · 2017
Cited alongside, same era.
Image-to-image translation with conditional adversarial networks
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros · 2017
Cited alongside, same era.
Progressive growing of gans for improved quality, stability, and variation
T. Karras, T. Aila, S. Laine, and J. Lehtinen · 2017
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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Thermometer encoding: One hot way to resist adversarial examples
J. Buckman, A. Roy, C. Raffel, and I. Goodfellow · 2018
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Stochastic activation pruning for robust adversarial defense
G. S. Dhillon, K. Azizzadenesheli, J. D. Bernstein, J. Kossaifi, A. Khanna, Z. C. Lipton, and A. Anandkumar · 2018
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Countering adversarial images using input transformations
C. Guo, M. Rana, M. Cisse, and L. van der Maaten · 2018
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Data augmentation by pairing samples for images classification
H. Inoue · 2018
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Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu · 2017
Cited alongside, same era.
Conditional image synthesis with auxiliary classifier gans
A. Odena, C. Olah, and J. Shlens · 2017
Cited alongside, same era.
Certifiable distributional robustness with principled adversarial training
A. Sinha, H. Namkoong, and J. Duchi · 2017
Cited alongside, same era.
Learning from between-class examples for deep sound recognition
Y. Tokozume, Y. Ushiku, and T. Harada · 2017
Cited alongside, same era.
Coulomb gans: Provably optimal nash equilibria via potential fields
T. Unterthiner, B. Nessler, G. Klambauer, M. Heusel, H. Ramsauer, and S. Hochreiter · 2017
Cited alongside, same era.
mixup: Beyond empirical risk minimization
H. Zhang, M. Cisse, Y. N. Dauphin, and D. Lopez-Paz · 2017
Cited alongside, same era.
Characterizing adversarial subspaces using local intrinsic dimensionality
X. Ma, B. Li, Y. Wang, S. M. Erfani, S. Wijewickrema, G. Schoenebeck, M. E. Houle, D. Song, and J. Bailey · 2018
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Spectral normalization for generative adversarial networks
T. Miyato, T. Kataoka, M. Koyama, and Y. Yoshida · 2018
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cGANs with projection discriminator
T. Miyato and M. Koyama · 2018
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Defense-GAN: Protecting classifiers against adversarial attacks using generative models
P. Samangouei, M. Kabkab, and R. Chellappa · 2018
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Defense-gan: Protecting classifiers against adversarial attacks using generative models
P. Samangouei, M. Kabkab, and R. Chellappa · 2018
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Pixeldefend: Leveraging generative models to understand and defend against adversarial examples
Y. Song, T. Kim, S. Nowozin, S. Ermon, and N. Kushman · 2018
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Generating adversarial examples with adversarial networks
C. Xiao, B. Li, J.-Y. Zhu, W. He, M. Liu, and D. Song · 2018
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Mitigating adversarial effects through randomization
C. Xie, J. Wang, Z. Zhang, Z. Ren, and A. Yuille · 2018
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