Fetching the paper…
Reading the bibliography…
Adversarial training is a common approach to improving the robustness of deep neural networks against adversarial examples.
AdverTorch v0.1: An adversarial robustness toolbox based on pytorch
Gavin Weiguang Ding, Luyu Wang, and Xiaomeng Jin · 1902
Earlier work this paper cites.
Improving generalization performance using double backpropagation
Harris Drucker and Yann Le Cun · 1992
Earlier work this paper cites.
Training with noise is equivalent to tikhonov regularization
Chris M Bishop · 1995
Earlier work this paper cites.
Heuristic algorithms for the unconstrained binary quadratic programming problem
John E Beasley · 1998
Earlier work this paper cites.
Robust optimization , volume 28
Aharon Ben-Tal, Laurent El Ghaoui, and Arkadi Nemirovski · 2009
Earlier work this paper cites.
Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
Earlier work this paper cites.
The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd edition)
Trevor Hastie, Robert Tibshirani, and Jerome Friedman · 2009
Earlier work this paper cites.
The Soar cognitive architecture
John E Laird · 2012
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
Earlier work this paper cites.
Learning with a strong adversary
Ruitong Huang, Bing Xu, Dale Schuurmans, and Csaba Szepesvári · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Earlier work this paper cites.
Adversarial manipulation of deep representations
Sara Sabour, Yanshuai Cao, Fartash Faghri, and David J Fleet · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Adversarial machine learning at scale
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2016
Cited alongside, same era.
Sergey Zagoruyko and Nikos Komodakis · 2016
Cited alongside, same era.
Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
Cited alongside, same era.
On the solution of nonconvex cardinality boolean quadratic programming problems: a computational study
Ricardo M Lima and Ignacio E Grossmann · 2017
Cited alongside, same era.
The space of transferable adversarial examples
Florian Tramèr, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel · 2017
Cited alongside, same era.
Batch normalization is a cause of adversarial vulnerability
Angus Galloway, Anna Golubeva, Thomas Tanay, Medhat Moussa, and Graham W Taylor · 2019
Later among the works it cites.
Simple black-box adversarial attacks
Chuan Guo, Jacob Gardner, Yurong You, Andrew Gordon Wilson, and Kilian Weinberger · 2019
Later among the works it cites.
Adversarial examples are not bugs, they are features
Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, and Aleksander Madry · 2019
Later among the works it cites.
Robustness via curvature regularization, and vice versa
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Jonathan Uesato, and Pascal Frossard · 2019
Later among the works it cites.
Adversarial robustness through local linearization
Chongli Qin, James Martens, Sven Gowal, Dilip Krishnan, Krishnamurthy Dvijotham, Alhussein Fawzi, Soham De, Robert Stanforth, and Pushmeet Kohli · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David Wagner · 2018
Cited alongside, same era.
Curriculum adversarial training
Qi-Zhi Cai, Chang Liu, and Dawn Song · 2018
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
Cited alongside, same era.
Adversarially robust generalization requires more data
Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, and Aleksander Madry · 2018
Cited alongside, same era.
Understanding adversarial training: Increasing local stability of supervised models through robust optimization
Uri Shaham, Yutaro Yamada, and Sahand Negahban · 2018
Cited alongside, same era.
Certifying some distributional robustness with principled adversarial training
Aman Sinha, Hongseok Namkoong, and John Duchi · 2018
Cited alongside, same era.
High-dimensional probability
Roman Vershynin · 2018
Cited alongside, same era.
First-order adversarial vulnerability of neural networks and input dimension
Carl-Johann Simon-Gabriel, Yann Ollivier, Leon Bottou, Bernhard Schölkopf, and David Lopez-Paz · 2019
Later among the works it cites.
Bilateral adversarial training: Towards fast training of more robust models against adversarial attacks
Jianyu Wang and Haichao Zhang · 2019
Later among the works it cites.
Theoretically principled trade-off between robustness and accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric Xing, Laurent El Ghaoui, and Michael Jordan · 2019
Later among the works it cites.
Square attack: a query-efficient black-box adversarial attack via random search
Maksym Andriushchenko, Francesco Croce, Nicolas Flammarion, and Matthias Hein · 2020
Closest in time.
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Francesco Croce and Matthias Hein · 2020
Closest in time.
Improving adversarial robustness through progressive hardening
Chawin Sitawarin, Supriyo Chakraborty, and David Wagner · 2020
Closest in time.
On adaptive attacks to adversarial example defenses
Florian Tramer, Nicholas Carlini, Wieland Brendel, and Aleksander Madry · 2020
Closest in time.
Improving adversarial robustness requires revisiting misclassified examples
Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma, and Quanquan Gu · 2020
Closest in time.
Adversarial robustness through local lipschitzness
Yao-Yuan Yang, Cyrus Rashtchian, Hongyang Zhang, Ruslan Salakhutdinov, and Kamalika Chaudhuri · 2020
Closest in time.