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We propose a novel technique to make neural network robust to adversarial examples using a generative adversarial network.
Learning multiple layers of features from tiny images
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Understanding the difficulty of training deep feedforward neural networks
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Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
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Imagenet classification with deep convolutional neural networks
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Intriguing properties of neural networks
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Adam: A method for stochastic optimization
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Striving for simplicity: The all convolutional net
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Dropout: a simple way to prevent neural networks from overfitting
N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
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Tensorflow: Large-scale machine learning on heterogeneous distributed systems, 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Distributional smoothing with virtual adversarial training
T. Miyato, S.-i. Maeda, M. Koyama, K. Nakae, and S. Ishii · 2015
U. Shaham, Y. Yamada, and S. Negahban · 2015
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Deep Learning
I. Goodfellow, Y. Bengio, and A. Courville · 2016
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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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Inception-v4, inception-resnet and the impact of residual connections on learning
C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Alemi · 2016
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BEGAN: Boundary equilibrium generative adversarial networks
D. Berthelot, T. Schumm, and L. Metz · 2017
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Cited alongside, same era.
Unsupervised representation learning with deep convolutional generative adversarial networks
A. Radford, L. Metz, and S. Chintala · 2015
Cited alongside, same era.
Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio
Cited in the paper.
Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy
Cited in the paper.