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Generating Less Certain Adversarial Examples Improves Robust Generalization
6 October 2023
Minxing Zhang
Michael Backes
Xiao Zhang
AAML
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Papers citing
"Generating Less Certain Adversarial Examples Improves Robust Generalization"
49 / 49 papers shown
CTBENCH: A Library and Benchmark for Certified Training
Yuhao Mao
Stefan Balauca
Martin Vechev
OOD
672
11
0
07 Jun 2024
CFA: Class-wise Calibrated Fair Adversarial Training
Computer Vision and Pattern Recognition (CVPR), 2023
Zeming Wei
Yifei Wang
Yiwen Guo
Yisen Wang
AAML
355
80
0
25 Mar 2023
Escaping limit cycles: Global convergence for constrained nonconvex-nonconcave minimax problems
International Conference on Learning Representations (ICLR), 2023
Thomas Pethick
P. Latafat
Panagiotis Patrinos
Olivier Fercoq
Volkan Cevher
331
60
0
20 Feb 2023
Better Diffusion Models Further Improve Adversarial Training
International Conference on Machine Learning (ICML), 2023
Zekai Wang
Tianyu Pang
Chao Du
Min Lin
Weiwei Liu
Shuicheng Yan
DiffM
590
297
0
09 Feb 2023
Exploring and Exploiting Decision Boundary Dynamics for Adversarial Robustness
International Conference on Learning Representations (ICLR), 2023
Yuancheng Xu
Yanchao Sun
Micah Goldblum
Tom Goldstein
Furong Huang
AAML
415
53
0
06 Feb 2023
Understanding Robust Overfitting of Adversarial Training and Beyond
International Conference on Machine Learning (ICML), 2022
Chaojian Yu
Bo Han
Li Shen
Jun Yu
Chen Gong
Biwei Huang
Tongliang Liu
OOD
283
78
0
17 Jun 2022
Adversarial Unlearning: Reducing Confidence Along Adversarial Directions
Neural Information Processing Systems (NeurIPS), 2022
Amrith Rajagopal Setlur
Benjamin Eysenbach
Virginia Smith
Sergey Levine
286
24
0
03 Jun 2022
Enhancing Adversarial Training with Second-Order Statistics of Weights
Computer Vision and Pattern Recognition (CVPR), 2022
Gao Jin
Xinping Yi
Wei Huang
S. Schewe
Xiaowei Huang
AAML
333
61
0
11 Mar 2022
Relating Adversarially Robust Generalization to Flat Minima
IEEE International Conference on Computer Vision (ICCV), 2021
David Stutz
Matthias Hein
Bernt Schiele
OOD
367
79
0
09 Apr 2021
Consistency Regularization for Adversarial Robustness
AAAI Conference on Artificial Intelligence (AAAI), 2021
Jihoon Tack
Sihyun Yu
Jongheon Jeong
Minseon Kim
Sung Ju Hwang
Jinwoo Shin
AAML
387
73
0
08 Mar 2021
Fixing Data Augmentation to Improve Adversarial Robustness
Sylvestre-Alvise Rebuffi
Sven Gowal
D. A. Calian
Florian Stimberg
Olivia Wiles
Timothy A. Mann
AAML
376
308
0
02 Mar 2021
Efficient Methods for Structured Nonconvex-Nonconcave Min-Max Optimization
International Conference on Artificial Intelligence and Statistics (AISTATS), 2020
Jelena Diakonikolas
C. Daskalakis
Sai Li
425
168
0
31 Oct 2020
RobustBench: a standardized adversarial robustness benchmark
Francesco Croce
Maksym Andriushchenko
Vikash Sehwag
Edoardo Debenedetti
Nicolas Flammarion
M. Chiang
Prateek Mittal
Matthias Hein
VLM
880
872
0
19 Oct 2020
Adversarial Training with Stochastic Weight Average
International Conference on Information Photonics (ICIP), 2020
Joong-won Hwang
Youngwan Lee
Sungchan Oh
Yuseok Bae
OOD
AAML
200
15
0
21 Sep 2020
Understanding and Improving Fast Adversarial Training
Maksym Andriushchenko
Nicolas Flammarion
AAML
498
339
0
06 Jul 2020
The limits of min-max optimization algorithms: convergence to spurious non-critical sets
Ya-Ping Hsieh
P. Mertikopoulos
Volkan Cevher
385
94
0
16 Jun 2020
Reevaluating Adversarial Examples in Natural Language
Findings (Findings), 2020
John X. Morris
Eli Lifland
Jack Lanchantin
Yangfeng Ji
Yanjun Qi
SILM
AAML
476
129
0
25 Apr 2020
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
International Conference on Machine Learning (ICML), 2020
Francesco Croce
Matthias Hein
AAML
889
2,310
0
03 Mar 2020
Overfitting in adversarially robust deep learning
International Conference on Machine Learning (ICML), 2020
Leslie Rice
Eric Wong
Zico Kolter
845
925
0
26 Feb 2020
Over-parameterized Adversarial Training: An Analysis Overcoming the Curse of Dimensionality
Neural Information Processing Systems (NeurIPS), 2020
Yi Zhang
Orestis Plevrakis
S. Du
Xingguo Li
Zhao Song
Sanjeev Arora
309
56
0
16 Feb 2020
Fast is better than free: Revisiting adversarial training
International Conference on Learning Representations (ICLR), 2020
Eric Wong
Leslie Rice
J. Zico Kolter
AAML
OOD
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1,340
0
12 Jan 2020
AdvHat: Real-world adversarial attack on ArcFace Face ID system
International Conference on Pattern Recognition (ICPR), 2019
Stepan Alekseevich Komkov
Aleksandr Petiushko
AAML
CVBM
269
345
0
23 Aug 2019
Convergence of Gradient Methods on Bilinear Zero-Sum Games
International Conference on Learning Representations (ICLR), 2019
Guojun Zhang
Yaoliang Yu
330
37
0
15 Aug 2019
Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systems
Pattern Recognition (Pattern Recognit.), 2019
Jiabo He
Yuhao Niu
Lin Gu
Yisen Wang
Yitian Zhao
James Bailey
Feng Lu
MedIm
AAML
399
527
0
24 Jul 2019
Convergence of Adversarial Training in Overparametrized Neural Networks
Neural Information Processing Systems (NeurIPS), 2019
Ruiqi Gao
Tianle Cai
Haochuan Li
Liwei Wang
Cho-Jui Hsieh
Jason D. Lee
AAML
436
115
0
19 Jun 2019
Unlabeled Data Improves Adversarial Robustness
Neural Information Processing Systems (NeurIPS), 2019
Y. Carmon
Aditi Raghunathan
Ludwig Schmidt
Abigail Z. Jacobs
John C. Duchi
609
800
0
31 May 2019
Are Labels Required for Improving Adversarial Robustness?
Neural Information Processing Systems (NeurIPS), 2019
J. Uesato
Jean-Baptiste Alayrac
Po-Sen Huang
Robert Stanforth
Alhussein Fawzi
Pushmeet Kohli
AAML
299
357
0
31 May 2019
Adversarial Examples Are Not Bugs, They Are Features
Neural Information Processing Systems (NeurIPS), 2019
Andrew Ilyas
Shibani Santurkar
Dimitris Tsipras
Logan Engstrom
Brandon Tran
Aleksander Madry
SILM
886
2,065
0
06 May 2019
Adversarial Training for Free!
Neural Information Processing Systems (NeurIPS), 2019
Ali Shafahi
Mahyar Najibi
Amin Ghiasi
Zheng Xu
John P. Dickerson
Christoph Studer
L. Davis
Gavin Taylor
Tom Goldstein
AAML
1.1K
1,422
0
29 Apr 2019
Certified Adversarial Robustness via Randomized Smoothing
Jeremy M. Cohen
Elan Rosenfeld
J. Zico Kolter
AAML
1.0K
2,413
0
08 Feb 2019
Theoretically Principled Trade-off between Robustness and Accuracy
Hongyang R. Zhang
Yaodong Yu
Jiantao Jiao
Eric Xing
L. Ghaoui
Sai Li
942
2,968
0
24 Jan 2019
Feature Denoising for Improving Adversarial Robustness
Cihang Xie
Yuxin Wu
Laurens van der Maaten
Alan Yuille
Kaiming He
517
1,004
0
09 Dec 2018
Theoretical Analysis of Adversarial Learning: A Minimax Approach
Zhuozhuo Tu
Jingwei Zhang
Dacheng Tao
AAML
305
75
0
13 Nov 2018
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin
Ming-Wei Chang
Kenton Lee
Kristina Toutanova
VLM
SSL
SSeg
3.1K
113,499
0
11 Oct 2018
The Limit Points of (Optimistic) Gradient Descent in Min-Max Optimization
C. Daskalakis
Ioannis Panageas
330
283
0
11 Jul 2018
Optimistic mirror descent in saddle-point problems: Going the extra (gradient) mile
International Conference on Learning Representations (ICLR), 2018
P. Mertikopoulos
Bruno Lecouat
Houssam Zenati
Chuan-Sheng Foo
V. Chandrasekhar
Georgios Piliouras
585
330
0
07 Jul 2018
Robustness May Be at Odds with Accuracy
Dimitris Tsipras
Shibani Santurkar
Logan Engstrom
Alexander Turner
Aleksander Madry
AAML
1.0K
1,923
0
30 May 2018
On the Suitability of
L
p
L_p
L
p
-norms for Creating and Preventing Adversarial Examples
Mahmood Sharif
Lujo Bauer
Michael K. Reiter
AAML
441
146
0
27 Feb 2018
Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality
Jiabo He
Yue Liu
Yisen Wang
S. Erfani
S. Wijewickrema
Grant Schoenebeck
Basel Alomair
Michael E. Houle
James Bailey
AAML
535
821
0
08 Jan 2018
Spatially Transformed Adversarial Examples
Chaowei Xiao
Jun-Yan Zhu
Yue Liu
Warren He
M. Liu
Basel Alomair
AAML
521
563
0
08 Jan 2018
Geometric robustness of deep networks: analysis and improvement
Can Kanbak
Seyed-Mohsen Moosavi-Dezfooli
P. Frossard
OOD
AAML
276
137
0
24 Nov 2017
Towards Deep Learning Models Resistant to Adversarial Attacks
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Aleksandar Makelov
Ludwig Schmidt
Dimitris Tsipras
Adrian Vladu
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14,396
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19 Jun 2017
Ensemble Adversarial Training: Attacks and Defenses
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Nicolas Papernot
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Dan Boneh
Patrick McDaniel
AAML
680
3,015
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Towards Evaluating the Robustness of Neural Networks
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D. Wagner
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Xinming Zhang
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Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
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Patrick McDaniel
Xi Wu
S. Jha
A. Swami
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513
3,271
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DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving
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Ari Seff
A. Kornhauser
Jianxiong Xiao
557
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Explaining and Harnessing Adversarial Examples
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Jonathon Shlens
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Intriguing properties of neural networks
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Wojciech Zaremba
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1
21 Dec 2013
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