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Efficient Robust Training via Backward Smoothing

Efficient Robust Training via Backward Smoothing

3 October 2020
Jinghui Chen
Yu Cheng
Zhe Gan
Quanquan Gu
Jingjing Liu
    AAML
ArXivPDFHTML

Papers citing "Efficient Robust Training via Backward Smoothing"

17 / 17 papers shown
Title
Democratic Training Against Universal Adversarial Perturbations
Bing-Jie Sun
Jun Sun
Wei Zhao
AAML
66
0
0
08 Feb 2025
Conserve-Update-Revise to Cure Generalization and Robustness Trade-off
  in Adversarial Training
Conserve-Update-Revise to Cure Generalization and Robustness Trade-off in Adversarial Training
Shruthi Gowda
Bahram Zonooz
Elahe Arani
AAML
31
2
0
26 Jan 2024
How Deep Learning Sees the World: A Survey on Adversarial Attacks &
  Defenses
How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses
Joana Cabral Costa
Tiago Roxo
Hugo Manuel Proença
Pedro R. M. Inácio
AAML
43
50
0
18 May 2023
Generating Adversarial Samples in Mini-Batches May Be Detrimental To
  Adversarial Robustness
Generating Adversarial Samples in Mini-Batches May Be Detrimental To Adversarial Robustness
T. Redgrave
Colton R. Crum
AAML
29
0
0
30 Mar 2023
Robust Models are less Over-Confident
Robust Models are less Over-Confident
Julia Grabinski
Paul Gavrikov
J. Keuper
M. Keuper
AAML
36
24
0
12 Oct 2022
Bag of Tricks for FGSM Adversarial Training
Bag of Tricks for FGSM Adversarial Training
Zichao Li
Li Liu
Zeyu Wang
Yuyin Zhou
Cihang Xie
AAML
33
6
0
06 Sep 2022
Adversarial Robustness through the Lens of Convolutional Filters
Adversarial Robustness through the Lens of Convolutional Filters
Paul Gavrikov
J. Keuper
38
15
0
05 Apr 2022
CNN Filter DB: An Empirical Investigation of Trained Convolutional
  Filters
CNN Filter DB: An Empirical Investigation of Trained Convolutional Filters
Paul Gavrikov
J. Keuper
AAML
24
31
0
29 Mar 2022
Robust Binary Models by Pruning Randomly-initialized Networks
Robust Binary Models by Pruning Randomly-initialized Networks
Chen Liu
Ziqi Zhao
Sabine Süsstrunk
Mathieu Salzmann
TPM
AAML
MQ
29
4
0
03 Feb 2022
On the Convergence and Robustness of Adversarial Training
On the Convergence and Robustness of Adversarial Training
Yisen Wang
Xingjun Ma
James Bailey
Jinfeng Yi
Bowen Zhou
Quanquan Gu
AAML
197
345
0
15 Dec 2021
On the Impact of Hard Adversarial Instances on Overfitting in
  Adversarial Training
On the Impact of Hard Adversarial Instances on Overfitting in Adversarial Training
Chen Liu
Zhichao Huang
Mathieu Salzmann
Tong Zhang
Sabine Süsstrunk
AAML
23
13
0
14 Dec 2021
BulletTrain: Accelerating Robust Neural Network Training via Boundary
  Example Mining
BulletTrain: Accelerating Robust Neural Network Training via Boundary Example Mining
Weizhe Hua
Yichi Zhang
Chuan Guo
Zhiru Zhang
G. E. Suh
OOD
39
15
0
29 Sep 2021
Exploring Misclassifications of Robust Neural Networks to Enhance
  Adversarial Attacks
Exploring Misclassifications of Robust Neural Networks to Enhance Adversarial Attacks
Leo Schwinn
René Raab
A. Nguyen
Dario Zanca
Bjoern M. Eskofier
AAML
14
58
0
21 May 2021
Learnable Boundary Guided Adversarial Training
Learnable Boundary Guided Adversarial Training
Jiequan Cui
Shu Liu
Liwei Wang
Jiaya Jia
OOD
AAML
30
124
0
23 Nov 2020
RobustBench: a standardized adversarial robustness benchmark
RobustBench: a standardized adversarial robustness benchmark
Francesco Croce
Maksym Andriushchenko
Vikash Sehwag
Edoardo Debenedetti
Nicolas Flammarion
M. Chiang
Prateek Mittal
Matthias Hein
VLM
234
678
0
19 Oct 2020
Sign-OPT: A Query-Efficient Hard-label Adversarial Attack
Sign-OPT: A Query-Efficient Hard-label Adversarial Attack
Minhao Cheng
Simranjit Singh
Patrick H. Chen
Pin-Yu Chen
Sijia Liu
Cho-Jui Hsieh
AAML
126
219
0
24 Sep 2019
Adversarial examples in the physical world
Adversarial examples in the physical world
Alexey Kurakin
Ian Goodfellow
Samy Bengio
SILM
AAML
287
5,842
0
08 Jul 2016
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