Improving Back-Propagation by Adding an Adversarial Gradient

Learning internal representations by error propagation

D. E. Rumelhart, G. E. Hinton, R. J. Williams · 1986

Earlier work this paper cites.

A training algorithm for optimal margin classifiers

Boser, Bernhard E., Guyon, Isabelle, and Vapnik, Vladimir · 1992

Earlier work this paper cites.

Noise injection into inputs in back-propagation learning

Matsuoka, Kiyotoshi · 1992

Earlier work this paper cites.

Best practices for convolutional neural networks applied to visual document analysis

Simard, Patrice Y., Steinkraus, David, and Platt, John C · 2003

Earlier work this paper cites.

A fast learning algorithm for deep belief nets

Hinton, Geoffrey E., Osindero, Simon, and Teh, Yee Whye · 2006

Earlier work this paper cites.

Extracting and composing robust features with denoising autoencoders

Vincent, Pascal, Larochelle, Hugo, Bengio, Yoshua, and Manzagol, Pierre-Antoine · 2008

Earlier work this paper cites.

Learning multiple layers of features from tiny images

Krizhevsky, Alex and Hinton, Geoffrey · 2009

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Deep boltzmann machines

Salakhutdinov, Ruslan and Hinton, Geoffrey E · 2009

Cited alongside, same era.

Intriguing properties of neural networks

Original

Szegedy, Christian, Zaremba, Wojciech, Sutskever, Ilya, Bruna, Joan, Erhan, Dumitru, Goodfellow, Ian J., and Fergus, Rob · 2013

Cited alongside, same era.

Regularization of neural networks using dropconnect

Wan, Li, Zeiler, Matthew D., Zhang, Sixin, LeCun, Yann, and Fergus, Rob · 2013

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Explaining and harnessing adversarial examples

Original

Goodfellow, Ian J., Shlens, Jonathon, and Szegedy, Christian · 2014

Cited alongside, same era.

Deep neural networks are easily fooled: High confidence predictions for unrecognizable images

Original

Nguyen, Anh Mai, Yosinski, Jason, and Clune, Jeff · 2014

Later among the works it cites.

Dropout: a simple way to prevent neural networks from overfitting

Srivastava, Nitish, Hinton, Geoffrey E., Krizhevsky, Alex, Sutskever, Ilya, and Salakhutdinov, Ruslan · 2014

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Analysis of classifiers’ robustness to adversarial perturbations

Original

Fawzi, Alhussein, Fawzi, Omar, and Frossard, Pascal · 2015

Closest in time.

Batch normalization: Accelerating deep network training by reducing internal covariate shift

Ioffe, Sergey and Szegedy, Christian · 2015

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Distributional smoothing by virtual adversarial examples

Original

Miyato, Takeru, Maeda, Shin-ichi, Koyama, Masanori, Nakae, Ken, and Ishii, Shin · 2015