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Theoretically Principled Trade-off between Robustness and Accuracy

Theoretically Principled Trade-off between Robustness and Accuracy

24 January 2019
Hongyang R. Zhang
Yaodong Yu
Jiantao Jiao
Eric Xing
L. Ghaoui
Michael I. Jordan
ArXivPDFHTML

Papers citing "Theoretically Principled Trade-off between Robustness and Accuracy"

50 / 595 papers shown
Title
Enhancing Diffusion-Based Image Synthesis with Robust Classifier
  Guidance
Enhancing Diffusion-Based Image Synthesis with Robust Classifier Guidance
Bahjat Kawar
Roy Ganz
Michael Elad
DiffM
29
38
0
18 Aug 2022
Two Heads are Better than One: Robust Learning Meets Multi-branch Models
Two Heads are Better than One: Robust Learning Meets Multi-branch Models
Dong Huang
Qi Bu
Yuhao Qing
Haowen Pi
Sen Wang
Heming Cui
OOD
AAML
32
0
0
17 Aug 2022
AWEncoder: Adversarial Watermarking Pre-trained Encoders in Contrastive
  Learning
AWEncoder: Adversarial Watermarking Pre-trained Encoders in Contrastive Learning
Tianxing Zhang
Hanzhou Wu
Xiaofeng Lu
Guangling Sun
AAML
27
4
0
08 Aug 2022
Improving Adversarial Robustness via Mutual Information Estimation
Improving Adversarial Robustness via Mutual Information Estimation
Dawei Zhou
Nannan Wang
Xinbo Gao
Bo Han
Xiaoyu Wang
Yibing Zhan
Tongliang Liu
AAML
19
15
0
25 Jul 2022
Jigsaw-ViT: Learning Jigsaw Puzzles in Vision Transformer
Jigsaw-ViT: Learning Jigsaw Puzzles in Vision Transformer
Yingyi Chen
Xiaoke Shen
Yahui Liu
Qinghua Tao
Johan A. K. Suykens
AAML
ViT
36
22
0
25 Jul 2022
Can we achieve robustness from data alone?
Can we achieve robustness from data alone?
Nikolaos Tsilivis
Jingtong Su
Julia Kempe
OOD
DD
38
18
0
24 Jul 2022
Decoupled Adversarial Contrastive Learning for Self-supervised
  Adversarial Robustness
Decoupled Adversarial Contrastive Learning for Self-supervised Adversarial Robustness
Chaoning Zhang
Kang Zhang
Chenshuang Zhang
Axi Niu
Jiu Feng
Chang D. Yoo
In So Kweon
SSL
40
24
0
22 Jul 2022
AugRmixAT: A Data Processing and Training Method for Improving Multiple
  Robustness and Generalization Performance
AugRmixAT: A Data Processing and Training Method for Improving Multiple Robustness and Generalization Performance
Xiaoliang Liu
S. Furao
Jian Zhao
Changhai Nie
AAML
15
1
0
21 Jul 2022
Adversarial Contrastive Learning via Asymmetric InfoNCE
Adversarial Contrastive Learning via Asymmetric InfoNCE
Qiying Yu
Jieming Lou
Xianyuan Zhan
Qizhang Li
W. Zuo
Yang Liu
Jingjing Liu
AAML
36
23
0
18 Jul 2022
Threat Model-Agnostic Adversarial Defense using Diffusion Models
Threat Model-Agnostic Adversarial Defense using Diffusion Models
Tsachi Blau
Roy Ganz
Bahjat Kawar
Alex M. Bronstein
Michael Elad
AAML
DiffM
27
26
0
17 Jul 2022
Aug-NeRF: Training Stronger Neural Radiance Fields with Triple-Level
  Physically-Grounded Augmentations
Aug-NeRF: Training Stronger Neural Radiance Fields with Triple-Level Physically-Grounded Augmentations
Tianlong Chen
Peihao Wang
Zhiwen Fan
Zhangyang Wang
36
55
0
04 Jul 2022
Removing Batch Normalization Boosts Adversarial Training
Removing Batch Normalization Boosts Adversarial Training
Haotao Wang
Aston Zhang
Shuai Zheng
Xingjian Shi
Mu Li
Zhangyang Wang
40
42
0
04 Jul 2022
Exact Spectral Norm Regularization for Neural Networks
Exact Spectral Norm Regularization for Neural Networks
Anton Johansson
Claes Strannegård
Niklas Engsner
P. Mostad
AAML
25
2
0
27 Jun 2022
Certifiably Robust Policy Learning against Adversarial Communication in
  Multi-agent Systems
Certifiably Robust Policy Learning against Adversarial Communication in Multi-agent Systems
Yanchao Sun
Ruijie Zheng
Parisa Hassanzadeh
Yongyuan Liang
S. Feizi
Sumitra Ganesh
Furong Huang
AAML
33
10
0
21 Jun 2022
On the Limitations of Stochastic Pre-processing Defenses
On the Limitations of Stochastic Pre-processing Defenses
Yue Gao
Ilia Shumailov
Kassem Fawaz
Nicolas Papernot
AAML
SILM
47
31
0
19 Jun 2022
Analysis and Extensions of Adversarial Training for Video Classification
Analysis and Extensions of Adversarial Training for Video Classification
K. A. Kinfu
René Vidal
AAML
33
13
0
16 Jun 2022
Queried Unlabeled Data Improves and Robustifies Class-Incremental
  Learning
Queried Unlabeled Data Improves and Robustifies Class-Incremental Learning
Tianlong Chen
Sijia Liu
Shiyu Chang
Lisa Amini
Zhangyang Wang
CLL
26
4
0
15 Jun 2022
Distributed Adversarial Training to Robustify Deep Neural Networks at
  Scale
Distributed Adversarial Training to Robustify Deep Neural Networks at Scale
Gaoyuan Zhang
Songtao Lu
Yihua Zhang
Xiangyi Chen
Pin-Yu Chen
Quanfu Fan
Lee Martie
L. Horesh
Min-Fong Hong
Sijia Liu
OOD
30
12
0
13 Jun 2022
SeATrans: Learning Segmentation-Assisted diagnosis model via Transformer
SeATrans: Learning Segmentation-Assisted diagnosis model via Transformer
Junde Wu
Huihui Fang
Fangxin Shang
Dalu Yang
Zhao-Yang Wang
Jing Gao
Yehui Yang
Yanwu Xu
MedIm
ViT
37
19
0
12 Jun 2022
Wavelet Regularization Benefits Adversarial Training
Wavelet Regularization Benefits Adversarial Training
Jun Yan
Huilin Yin
Xiaoyang Deng
Zi-qin Zhao
Wancheng Ge
Hao Zhang
Gerhard Rigoll
AAML
19
2
0
08 Jun 2022
Building Robust Ensembles via Margin Boosting
Building Robust Ensembles via Margin Boosting
Dinghuai Zhang
Hongyang R. Zhang
Aaron Courville
Yoshua Bengio
Pradeep Ravikumar
A. Suggala
AAML
UQCV
48
15
0
07 Jun 2022
Vanilla Feature Distillation for Improving the Accuracy-Robustness
  Trade-Off in Adversarial Training
Vanilla Feature Distillation for Improving the Accuracy-Robustness Trade-Off in Adversarial Training
Guodong Cao
Peng Kuang
Xiaowei Dong
Zhifei Zhang
Hengchang Guo
Zhan Qin
Kui Ren
AAML
30
1
0
05 Jun 2022
Attack-Agnostic Adversarial Detection
Attack-Agnostic Adversarial Detection
Jiaxin Cheng
Mohamed Hussein
J. Billa
Wael AbdAlmageed
AAML
28
0
0
01 Jun 2022
Guided Diffusion Model for Adversarial Purification
Guided Diffusion Model for Adversarial Purification
Jinyi Wang
Zhaoyang Lyu
Dahua Lin
Bo Dai
Hongfei Fu
DiffM
196
83
0
30 May 2022
Robust Weight Perturbation for Adversarial Training
Robust Weight Perturbation for Adversarial Training
Chaojian Yu
Bo Han
Biwei Huang
Li Shen
Shiming Ge
Bo Du
Tongliang Liu
AAML
22
33
0
30 May 2022
Semi-supervised Semantics-guided Adversarial Training for Trajectory
  Prediction
Semi-supervised Semantics-guided Adversarial Training for Trajectory Prediction
Ruochen Jiao
Xiangguo Liu
Takami Sato
Qi Alfred Chen
Qi Zhu
AAML
43
20
0
27 May 2022
Why Robust Generalization in Deep Learning is Difficult: Perspective of
  Expressive Power
Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power
Binghui Li
Jikai Jin
Han Zhong
J. Hopcroft
Liwei Wang
OOD
84
27
0
27 May 2022
How explainable are adversarially-robust CNNs?
How explainable are adversarially-robust CNNs?
Mehdi Nourelahi
Lars Kotthoff
Peijie Chen
Anh Totti Nguyen
AAML
FAtt
24
8
0
25 May 2022
Squeeze Training for Adversarial Robustness
Squeeze Training for Adversarial Robustness
Qizhang Li
Yiwen Guo
W. Zuo
Hao Chen
OOD
49
9
0
23 May 2022
Hierarchical Distribution-Aware Testing of Deep Learning
Hierarchical Distribution-Aware Testing of Deep Learning
Wei Huang
Xingyu Zhao
Alec Banks
V. Cox
Xiaowei Huang
OOD
AAML
44
10
0
17 May 2022
Diffusion Models for Adversarial Purification
Diffusion Models for Adversarial Purification
Weili Nie
Brandon Guo
Yujia Huang
Chaowei Xiao
Arash Vahdat
Anima Anandkumar
WIGM
221
422
0
16 May 2022
Learn2Weight: Parameter Adaptation against Similar-domain Adversarial
  Attacks
Learn2Weight: Parameter Adaptation against Similar-domain Adversarial Attacks
Siddhartha Datta
AAML
36
4
0
15 May 2022
Smooth-Reduce: Leveraging Patches for Improved Certified Robustness
Smooth-Reduce: Leveraging Patches for Improved Certified Robustness
Ameya Joshi
Minh Pham
Minsu Cho
Leonid Boytsov
Filipe Condessa
J. Zico Kolter
C. Hegde
UQCV
AAML
32
2
0
12 May 2022
CE-based white-box adversarial attacks will not work using super-fitting
CE-based white-box adversarial attacks will not work using super-fitting
Youhuan Yang
Lei Sun
Leyu Dai
Song Guo
Xiuqing Mao
Xiaoqin Wang
Bayi Xu
AAML
37
0
0
04 May 2022
Backdooring Explainable Machine Learning
Backdooring Explainable Machine Learning
Maximilian Noppel
Lukas Peter
Christian Wressnegger
AAML
18
5
0
20 Apr 2022
SETTI: A Self-supervised Adversarial Malware Detection Architecture in
  an IoT Environment
SETTI: A Self-supervised Adversarial Malware Detection Architecture in an IoT Environment
Marjan Golmaryami
R. Taheri
Zahra Pooranian
Mohammad Shojafar
Pei Xiao
33
12
0
16 Apr 2022
Revisiting the Adversarial Robustness-Accuracy Tradeoff in Robot
  Learning
Revisiting the Adversarial Robustness-Accuracy Tradeoff in Robot Learning
Mathias Lechner
Alexander Amini
Daniela Rus
T. Henzinger
AAML
34
10
0
15 Apr 2022
A Simple Approach to Adversarial Robustness in Few-shot Image
  Classification
A Simple Approach to Adversarial Robustness in Few-shot Image Classification
Akshayvarun Subramanya
Hamed Pirsiavash
VLM
29
6
0
11 Apr 2022
The Effects of Regularization and Data Augmentation are Class Dependent
The Effects of Regularization and Data Augmentation are Class Dependent
Randall Balestriero
Léon Bottou
Yann LeCun
41
94
0
07 Apr 2022
Adversarial Robustness through the Lens of Convolutional Filters
Adversarial Robustness through the Lens of Convolutional Filters
Paul Gavrikov
J. Keuper
40
15
0
05 Apr 2022
Improving Vision Transformers by Revisiting High-frequency Components
Improving Vision Transformers by Revisiting High-frequency Components
Jiawang Bai
Liuliang Yuan
Shutao Xia
Shuicheng Yan
Zhifeng Li
Wen Liu
ViT
16
90
0
03 Apr 2022
Robust and Accurate -- Compositional Architectures for Randomized
  Smoothing
Robust and Accurate -- Compositional Architectures for Randomized Smoothing
Miklós Z. Horváth
Mark Niklas Muller
Marc Fischer
Martin Vechev
UQCV
AAML
8
13
0
01 Apr 2022
Towards Robust Rain Removal Against Adversarial Attacks: A Comprehensive
  Benchmark Analysis and Beyond
Towards Robust Rain Removal Against Adversarial Attacks: A Comprehensive Benchmark Analysis and Beyond
Yi Yu
Wenhan Yang
Yap-Peng Tan
Alex C. Kot
AAML
39
61
0
31 Mar 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
How to Robustify Black-Box ML Models? A Zeroth-Order Optimization
  Perspective
How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective
Yimeng Zhang
Yuguang Yao
Jinghan Jia
Jinfeng Yi
Min-Fong Hong
Shiyu Chang
Sijia Liu
AAML
26
33
0
27 Mar 2022
A Survey of Robust Adversarial Training in Pattern Recognition:
  Fundamental, Theory, and Methodologies
A Survey of Robust Adversarial Training in Pattern Recognition: Fundamental, Theory, and Methodologies
Zhuang Qian
Kaizhu Huang
Qiufeng Wang
Xu-Yao Zhang
OOD
AAML
ObjD
54
72
0
26 Mar 2022
A Unified Contrastive Energy-based Model for Understanding the
  Generative Ability of Adversarial Training
A Unified Contrastive Energy-based Model for Understanding the Generative Ability of Adversarial Training
Yifei Wang
Yisen Wang
Jiansheng Yang
Zhouchen Lin
AAML
29
13
0
25 Mar 2022
A Manifold View of Adversarial Risk
A Manifold View of Adversarial Risk
Wen-jun Zhang
Yikai Zhang
Xiaoling Hu
Mayank Goswami
Chao Chen
Dimitris N. Metaxas
AAML
19
6
0
24 Mar 2022
Self-Ensemble Adversarial Training for Improved Robustness
Self-Ensemble Adversarial Training for Improved Robustness
Hongjun Wang
Yisen Wang
OOD
AAML
20
48
0
18 Mar 2022
Robustness through Cognitive Dissociation Mitigation in Contrastive
  Adversarial Training
Robustness through Cognitive Dissociation Mitigation in Contrastive Adversarial Training
Adir Rahamim
I. Naeh
AAML
30
1
0
16 Mar 2022
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