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In this paper we aim to explore the general robustness of neural network classifiers by utilizing adversarial as well as natural perturbations.
Theoretically principled trade-off between robustness and accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P Xing, Laurent El Ghaoui, and Michael I Jordan · 1901
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Huan Zhang, Hongge Chen, Zhao Song, Duane Boning, Inderjit S Dhillon, and Cho-Jui Hsieh · 1901
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Nic Ford, Justin Gilmer, Nicolas Carlini, and Dogus Cubuk · 2019
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Dan Hendrycks and Thomas Dietterich · 2019
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Daniel Kang, Yi Sun, Dan Hendrycks, Tom Brown, and Jacob Steinhardt · 2019
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Are adversarial robustness and common perturbation robustness independant attributes?
Alfred Laugros, Alice Caplier, and Matthieu Ospici · 2019
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Robust local features for improving the generalization of adversarial training
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