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Many deep Convolutional Neural Networks (CNN) make incorrect predictions on adversarial samples obtained by imperceptible perturbations of clean samples.
Multivariable fuctional interpolation and adaptive networks
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Rectified linear units improve restricted boltzmann machines
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Unsupervised clustering with spiking neurons by sparse temporal coding and multilayer rbf networks
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Scalable training of l1-regularized log-linear models
Andrew, G. and Gao, J · 2007
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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Maxout networks
Goodfellow, I. J., Warde-Farley, D., Mirza, M., Courville, A., and Bengio, Y · 2013
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2014
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Visualizing and understanding convolutional networks
Zeiler, M. D. and Fergus, R · 2014
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Visual causal feature learning
Chalupka, K., Perona, P., and Eberhardt, F · 2015
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Batch normalization: accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
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Makhzani, A., Shlens, J., Jaitly, N., and Goodfellow, I · 2015
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Nguyen, A., Yosinski, J., and Clune, J · 2015
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Cifar-10
CIFAR · 2016
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Distributional smoothing by virtual adversarial examples
Miyato, T., Maeda, S. I., Koyama, M., Nakae, K., and Ishii, S · 2016
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Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., and Chintala, S · 2016
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Towards deep neural network architectures robust to adversarial examples
Gu, S. and Rigazio, L · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2015
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Adversarial manipulation of deep representations
Sabour, S., Cao, Y., Faghri, F., and Fleet, D. J · 2016
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Unsupervised and semi-supervised learning with categorical generative adversarial networks
Springenberg, J. T · 2016
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Vedaldi, A · 2016
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