Fetching the paper…
Reading the bibliography…
Regularizing the gradient norm of the output of a neural network with respect to its inputs is a powerful technique, rediscovered several times.
Spline Models for Observational Data
G. Wahba · 1990
Earlier work this paper cites.
Double backpropagation: Increasing generalization performance
H. Drucker and Y LeCun · 1991
Earlier work this paper cites.
Tangent prop - A formalism for specifying selected invariances in an adaptive network
Patrice Y. Simard, Bernard Victorri, Yann LeCun, and John S. Denker · 1991
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
A Distribution-Free Theory of Nonparametric Regression
László Györfi, Michael Kohler, Adam Krzyzak, and Harro Walk · 2002
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
Earlier work this paper cites.
Nonparametric sparsity and regularization
Lorenzo Rosasco, Silvia Villa, Sofia Mosci, Matteo Santoro, and Alessandro Verri · 2013
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus · 2013
Earlier work this paper cites.
Towards deep neural network architectures robust to adversarial examples
Shixiang Gu and Luca Rigazio · 2014
Cited alongside, same era.
Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Cited alongside, same era.
Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna · 2016
Cited alongside, same era.
Regularizing neural networks by penalizing confident output distributions
Gabriel Pereyra, George Tucker, Jan Chorowski, Lukasz Kaiser, and Geoffrey E. Hinton · 2017
Closest in time.
Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients
A. Slavin Ross and F. Doshi-Velez · 2017
Closest in time.
Robust large margin deep neural networks
Jure Sokolic, Raja Giryes, Guillermo Sapiro, and Miguel R. D. Rodrigues · 2017
Closest in time.
Spectral norm regularization for improving the generalizability of deep learning
Yuichi Yoshida and Takeru Miyato · 2017
Closest in time.
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cissé, Yann N. Dauphin, and David Lopez-Paz · 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Sobolev training for neural networks
Wojciech M. Czarnecki, Simon Osindero, Max Jaderberg, Grzegorz Swirszcz, and Razvan Pascanu · 2017
Cited alongside, same era.
Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron Courville · 2017
Cited alongside, same era.
Unifying adversarial training algorithms with data gradient regularization
Alexander G. Ororbia II, Daniel Kifer, and C. Lee Giles · 2017
Cited alongside, same era.
Closest in time.
Improving DNN robustness to adversarial attacks using jacobian regularization
Daniel Jakubovitz and Raja Giryes · 2018
Closest in time.
Sensitivity and generalization in neural networks: an empirical study
Roman Novak, Yasaman Bahri, Daniel A. Abolafia, Jeffrey Pennington, and Jascha Sohl-Dickstein · 2018
Closest in time.