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2004.08628
Cited By
Single-step Adversarial training with Dropout Scheduling
Computer Vision and Pattern Recognition (CVPR), 2020
18 April 2020
S. VivekB.
R. Venkatesh Babu
OOD
AAML
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Papers citing
"Single-step Adversarial training with Dropout Scheduling"
36 / 36 papers shown
FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model Fusion
Neural Information Processing Systems (NeurIPS), 2024
Zhenheng Tang
Yonggang Zhang
Peijie Dong
Yiu-ming Cheung
Amelie Chi Zhou
Bo Han
Xiaowen Chu
FedML
MoMe
AI4CE
380
19
0
27 Oct 2024
On Using Certified Training towards Empirical Robustness
Alessandro De Palma
Serge Durand
Zakaria Chihani
François Terrier
Caterina Urban
OOD
AAML
456
3
0
02 Oct 2024
MIMIR: Masked Image Modeling for Mutual Information-based Adversarial Robustness
Xiaoyun Xu
Shujian Yu
Jingzheng Wu
S. Picek
AAML
697
10
0
08 Dec 2023
Fast Propagation is Better: Accelerating Single-Step Adversarial Training via Sampling Subnetworks
IEEE Transactions on Information Forensics and Security (IEEE TIFS), 2023
Yang Liu
Jianshu Li
Jindong Gu
Yang Bai
Xiaochun Cao
AAML
263
14
0
24 Oct 2023
Adversarial-Aware Deep Learning System based on a Secondary Classical Machine Learning Verification Approach
Italian National Conference on Sensors (INS), 2023
Mohammed Alkhowaiter
Hisham A. Kholidy
Mnassar Alyami
Abdulmajeed Alghamdi
C. Zou
AAML
306
12
0
01 Jun 2023
A Survey on the Robustness of Computer Vision Models against Common Corruptions
Shunxin Wang
Raymond N. J. Veldhuis
Christoph Brune
N. Strisciuglio
OOD
VLM
735
27
0
10 May 2023
Do we need entire training data for adversarial training?
Vipul Gupta
Apurva Narayan
AAML
275
2
0
10 Mar 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
580
298
0
09 Feb 2023
Explainability and Robustness of Deep Visual Classification Models
Jindong Gu
AAML
318
2
0
03 Jan 2023
Stable and Efficient Adversarial Training through Local Linearization
Zhuorong Li
Daiwei Yu
AAML
136
0
0
11 Oct 2022
Designing and Training of Lightweight Neural Networks on Edge Devices using Early Halting in Knowledge Distillation
IEEE Transactions on Mobile Computing (IEEE TMC), 2022
Rahul Mishra
Hari Prabhat Gupta
214
19
0
30 Sep 2022
SegPGD: An Effective and Efficient Adversarial Attack for Evaluating and Boosting Segmentation Robustness
European Conference on Computer Vision (ECCV), 2022
Jindong Gu
Hengshuang Zhao
Volker Tresp
Juil Sock
AAML
325
96
0
25 Jul 2022
Towards Efficient Adversarial Training on Vision Transformers
European Conference on Computer Vision (ECCV), 2022
Boxi Wu
Jindong Gu
Zhifeng Li
Deng Cai
Xiaofei He
Wei Liu
ViT
AAML
296
45
0
21 Jul 2022
Improving the Robustness and Generalization of Deep Neural Network with Confidence Threshold Reduction
Xiangyuan Yang
Jie Lin
Hanlin Zhang
Xinyu Yang
Peng Zhao
AAML
OOD
314
1
0
02 Jun 2022
Robustness and Accuracy Could Be Reconcilable by (Proper) Definition
International Conference on Machine Learning (ICML), 2022
Tianyu Pang
Min Lin
Xiao Yang
Junyi Zhu
Shuicheng Yan
554
163
0
21 Feb 2022
Make Some Noise: Reliable and Efficient Single-Step Adversarial Training
Neural Information Processing Systems (NeurIPS), 2022
Pau de Jorge
Adel Bibi
Riccardo Volpi
Amartya Sanyal
Juil Sock
Grégory Rogez
P. Dokania
AAML
386
59
0
02 Feb 2022
Rethinking Feature Uncertainty in Stochastic Neural Networks for Adversarial Robustness
Hao Yang
Min Wang
Zhengfei Yu
Yun Zhou
OOD
AAML
232
3
0
01 Jan 2022
Subspace Adversarial Training
Tao Li
Yingwen Wu
Sizhe Chen
Kun Fang
Xiaolin Huang
AAML
OOD
376
68
0
24 Nov 2021
Get Fooled for the Right Reason: Improving Adversarial Robustness through a Teacher-guided Curriculum Learning Approach
A. Sarkar
Anirban Sarkar
Sowrya Gali
V. Balasubramanian
AAML
259
8
0
30 Oct 2021
Advances in adversarial attacks and defenses in computer vision: A survey
Naveed Akhtar
Lin Wang
Navid Kardan
M. Shah
AAML
543
315
0
01 Aug 2021
Multi-stage Optimization based Adversarial Training
Xiaosen Wang
Chuanbiao Song
Liwei Wang
Kun He
AAML
189
5
0
26 Jun 2021
Robustifying
ℓ
∞
\ell_\infty
ℓ
∞
Adversarial Training to the Union of Perturbation Models
Ameya D. Patil
Michael Tuttle
Alex Schwing
Naresh R Shanbhag
AAML
260
0
0
31 May 2021
NoiLIn: Improving Adversarial Training and Correcting Stereotype of Noisy Labels
Jingfeng Zhang
Xilie Xu
Bo Han
Tongliang Liu
Gang Niu
Li-zhen Cui
Masashi Sugiyama
NoLa
AAML
268
9
0
31 May 2021
Relating Adversarially Robust Generalization to Flat Minima
IEEE International Conference on Computer Vision (ICCV), 2021
David Stutz
Matthias Hein
Bernt Schiele
OOD
362
78
0
09 Apr 2021
Reliably fast adversarial training via latent adversarial perturbation
IEEE International Conference on Computer Vision (ICCV), 2021
Geon Yeong Park
Sang Wan Lee
AAML
270
34
0
04 Apr 2021
ZeroGrad : Mitigating and Explaining Catastrophic Overfitting in FGSM Adversarial Training
Zeinab Golgooni
Mehrdad Saberi
Masih Eskandar
M. Rohban
AAML
133
17
0
29 Mar 2021
Guided Interpolation for Adversarial Training
Chen Chen
Jingfeng Zhang
Xilie Xu
Tianlei Hu
Gang Niu
Gang Chen
Masashi Sugiyama
AAML
206
10
0
15 Feb 2021
Robust Single-step Adversarial Training with Regularizer
Chinese Conference on Pattern Recognition and Computer Vision (CPRCV), 2021
Lehui Xie
Yaopeng Wang
Jianwei Yin
Ximeng Liu
AAML
127
1
0
05 Feb 2021
Recent Advances in Adversarial Training for Adversarial Robustness
International Joint Conference on Artificial Intelligence (IJCAI), 2021
Tao Bai
Jinqi Luo
Jun Zhao
Bihan Wen
Qian Wang
AAML
646
611
0
02 Feb 2021
Weight-Covariance Alignment for Adversarially Robust Neural Networks
International Conference on Machine Learning (ICML), 2020
Panagiotis Eustratiadis
Henry Gouk
Da Li
Timothy M. Hospedales
OOD
AAML
410
24
0
17 Oct 2020
Understanding Catastrophic Overfitting in Single-step Adversarial Training
AAAI Conference on Artificial Intelligence (AAAI), 2020
Hoki Kim
Woojin Lee
Jaewook Lee
AAML
509
131
0
05 Oct 2020
Bag of Tricks for Adversarial Training
International Conference on Learning Representations (ICLR), 2020
Tianyu Pang
Xiao Yang
Yinpeng Dong
Hang Su
Jun Zhu
AAML
455
274
0
01 Oct 2020
Odyssey: Creation, Analysis and Detection of Trojan Models
IEEE Transactions on Information Forensics and Security (IEEE TIFS), 2020
Marzieh Edraki
Nazmul Karim
Nazanin Rahnavard
Lin Wang
M. Shah
AAML
280
15
0
16 Jul 2020
Understanding and Improving Fast Adversarial Training
Maksym Andriushchenko
Nicolas Flammarion
AAML
497
339
0
06 Jul 2020
Attacks Which Do Not Kill Training Make Adversarial Learning Stronger
International Conference on Machine Learning (ICML), 2020
Jingfeng Zhang
Xilie Xu
Bo Han
Gang Niu
Li-zhen Cui
Masashi Sugiyama
Mohan S. Kankanhalli
AAML
217
450
0
26 Feb 2020
Boosting Adversarial Training with Hypersphere Embedding
Neural Information Processing Systems (NeurIPS), 2020
Tianyu Pang
Xiao Yang
Yinpeng Dong
Kun Xu
Jun Zhu
Hang Su
AAML
442
161
0
20 Feb 2020
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