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In many classification problems a classifier should be robust to small variations in the input vector.
Training with noise is equivalent to Tikhonov regularization
Bishop, Christopher M · 1995
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Gradient-based learning applied to document recognition
LeCun, Yann, Bottou, Léon, Bengio, Yoshua, and Haffner, Patrick · 1998
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Learning multiple layers of features from tiny images
Krizhevsky, Alex · 2009
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Contractive auto-encoders: Explicit invariance during feature extraction
Rifai, Salah, Vincent, Pascal, Muller, Xavier, Glorot, Xavier, and Bengio, Yoshua · 2011
Earlier work this paper cites.
Transformation invariance in pattern recognition–tangent distance and tangent propagation
Simard, Patrice Y, LeCun, Yann A, Denker, John S, and Victorri, Bernard · 2012
Cited alongside, same era.
Intriguing properties of neural networks
Szegedy, Christian, Zaremba, Wojciech, Sutskever, Ilya, Bruna, Joan, Erhan, Dumitru, Goodfellow, Ian, and Fergus, Rob · 2013
Cited alongside, same era.
Explaining and harnessing adversarial examples
Goodfellow, Ian J, Shlens, Jonathon, and Szegedy, Christian · 2014
Cited alongside, same era.
Deep visual-semantic alignments for generating image descriptions
Karpathy, Andrej and Fei-Fei, Li · 2014
Cited alongside, same era.
Lee, Chen-Yu, Xie, Saining, Gallagher, Patrick, Zhang, Zhengyou, and Tu, Zhuowen · 2014
Later among the works it cites.
Going deeper with convolutions
Szegedy, Christian, Liu, Wei, Jia, Yangqing, Sermanet, Pierre, Reed, Scott, Anguelov, Dragomir, Erhan, Dumitru, Vanhoucke, Vincent, and Rabinovich, Andrew · 2014
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Fundamental limits on adversarial robustness
Fawzi, Alhussein, Fawzi, Omar, and Frossard, Pascal · 2015
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