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1905.02175
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Adversarial Examples Are Not Bugs, They Are Features
Neural Information Processing Systems (NeurIPS), 2019
6 May 2019
Andrew Ilyas
Shibani Santurkar
Dimitris Tsipras
Logan Engstrom
Brandon Tran
Aleksander Madry
SILM
Re-assign community
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Papers citing
"Adversarial Examples Are Not Bugs, They Are Features"
50 / 1,093 papers shown
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Blind Backdoors in Deep Learning Models
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International Conference on Web and Social Media (ICWSM), 2020
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Indira Sen
Julian Kohne
Fabian Flöck
Claudia Wagner
269
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Towards Accurate and Robust Domain Adaptation under Noisy Environments
International Joint Conference on Artificial Intelligence (IJCAI), 2020
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Xian-Jin Gui
C. Cui
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Adversarial Attacks and Defenses: An Interpretation Perspective
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211
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Jared Kaplan
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Provably robust deep generative models
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162
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Dynamic Knowledge Graph-based Dialogue Generation with Improved Adversarial Meta-Learning
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J. Bao
Gaojie Zhang
184
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Shortcut Learning in Deep Neural Networks
Nature Machine Intelligence (NMI), 2020
Robert Geirhos
J. Jacobsen
Claudio Michaelis
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Wieland Brendel
Matthias Bethge
Felix Wichmann
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Adversarial Robustness Guarantees for Random Deep Neural Networks
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Giacomo De Palma
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S. Lloyd
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145
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13 Apr 2020
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171
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Transferable, Controllable, and Inconspicuous Adversarial Attacks on Person Re-identification With Deep Mis-Ranking
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Hongjun Wang
Guangrun Wang
Ya Li
Dongyu Zhang
Liang Lin
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147
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Leo Horne
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118
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Fartash Faghri
Nicolas Papernot
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214
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Jongheon Jeong
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192
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225
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126
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196
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62
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David K Gifford
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259
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Christian Berghoff
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206
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115
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203
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211
4
0
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Out-of-Distribution Generalization via Risk Extrapolation (REx)
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David M. Krueger
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Xiao Zhang
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257
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48
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Randomization matters. How to defend against strong adversarial attacks
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278
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Attacks Which Do Not Kill Training Make Adversarial Learning Stronger
International Conference on Machine Learning (ICML), 2020
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Mohan S. Kankanhalli
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166
441
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262
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159
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371
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