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Robustness May Be at Odds with Accuracy

Robustness May Be at Odds with Accuracy

30 May 2018
Dimitris Tsipras
Shibani Santurkar
Logan Engstrom
Alexander Turner
A. Madry
    AAML
ArXivPDFHTML

Papers citing "Robustness May Be at Odds with Accuracy"

50 / 439 papers shown
Title
Interpolated Joint Space Adversarial Training for Robust and
  Generalizable Defenses
Interpolated Joint Space Adversarial Training for Robust and Generalizable Defenses
Chun Pong Lau
Jiang-Long Liu
Hossein Souri
Wei-An Lin
S. Feizi
Ramalingam Chellappa
AAML
31
12
0
12 Dec 2021
PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures
PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures
Dan Hendrycks
Andy Zou
Mantas Mazeika
Leonard Tang
Bo-wen Li
D. Song
Jacob Steinhardt
UQCV
25
137
0
09 Dec 2021
Mutual Adversarial Training: Learning together is better than going
  alone
Mutual Adversarial Training: Learning together is better than going alone
Jiang-Long Liu
Chun Pong Lau
Hossein Souri
S. Feizi
Ramalingam Chellappa
OOD
AAML
48
24
0
09 Dec 2021
Image classifiers can not be made robust to small perturbations
Image classifiers can not be made robust to small perturbations
Zheng Dai
David K Gifford
VLM
AAML
36
1
0
07 Dec 2021
On the Existence of the Adversarial Bayes Classifier (Extended Version)
On the Existence of the Adversarial Bayes Classifier (Extended Version)
Pranjal Awasthi
Natalie Frank
M. Mohri
31
24
0
03 Dec 2021
Adv-4-Adv: Thwarting Changing Adversarial Perturbations via Adversarial
  Domain Adaptation
Adv-4-Adv: Thwarting Changing Adversarial Perturbations via Adversarial Domain Adaptation
Tianyue Zheng
Zhe Chen
Shuya Ding
Chao Cai
Jun Luo
AAML
35
5
0
01 Dec 2021
$\ell_\infty$-Robustness and Beyond: Unleashing Efficient Adversarial
  Training
ℓ∞\ell_\inftyℓ∞​-Robustness and Beyond: Unleashing Efficient Adversarial Training
H. M. Dolatabadi
S. Erfani
C. Leckie
OOD
AAML
29
11
0
01 Dec 2021
Pyramid Adversarial Training Improves ViT Performance
Pyramid Adversarial Training Improves ViT Performance
Charles Herrmann
Kyle Sargent
Lu Jiang
Ramin Zabih
Huiwen Chang
Ce Liu
Dilip Krishnan
Deqing Sun
ViT
32
56
0
30 Nov 2021
Image prediction of disease progression by style-based manifold
  extrapolation
Image prediction of disease progression by style-based manifold extrapolation
T. Han
Jakob Nikolas Kather
F. Pedersoli
M. Zimmermann
S. Keil
...
Fabian Kiessling
Volkmar Schulz
Christiane Kuhl
S. Nebelung
Daniel Truhn
MedIm
27
3
0
22 Nov 2021
Medical Aegis: Robust adversarial protectors for medical images
Medical Aegis: Robust adversarial protectors for medical images
Qingsong Yao
Zecheng He
S. Kevin Zhou
AAML
MedIm
30
2
0
22 Nov 2021
MixACM: Mixup-Based Robustness Transfer via Distillation of Activated
  Channel Maps
MixACM: Mixup-Based Robustness Transfer via Distillation of Activated Channel Maps
Muhammad Awais
Fengwei Zhou
Chuanlong Xie
Jiawei Li
Sung-Ho Bae
Zhenguo Li
AAML
43
17
0
09 Nov 2021
Pareto Adversarial Robustness: Balancing Spatial Robustness and
  Sensitivity-based Robustness
Pareto Adversarial Robustness: Balancing Spatial Robustness and Sensitivity-based Robustness
Ke Sun
Mingjie Li
Zhouchen Lin
AAML
27
2
0
03 Nov 2021
Get Fooled for the Right Reason: Improving Adversarial Robustness
  through a Teacher-guided Curriculum Learning Approach
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
35
7
0
30 Oct 2021
CAP: Co-Adversarial Perturbation on Weights and Features for Improving
  Generalization of Graph Neural Networks
CAP: Co-Adversarial Perturbation on Weights and Features for Improving Generalization of Graph Neural Networks
Hao Xue
Kaixiong Zhou
Tianlong Chen
Kai Guo
Xia Hu
Yi Chang
Xin Wang
AAML
32
15
0
28 Oct 2021
AugMax: Adversarial Composition of Random Augmentations for Robust
  Training
AugMax: Adversarial Composition of Random Augmentations for Robust Training
Haotao Wang
Chaowei Xiao
Jean Kossaifi
Zhiding Yu
Anima Anandkumar
Zhangyang Wang
32
107
0
26 Oct 2021
Improving Robustness using Generated Data
Improving Robustness using Generated Data
Sven Gowal
Sylvestre-Alvise Rebuffi
Olivia Wiles
Florian Stimberg
D. A. Calian
Timothy A. Mann
36
294
0
18 Oct 2021
Combining Diverse Feature Priors
Combining Diverse Feature Priors
Saachi Jain
Dimitris Tsipras
A. Madry
69
14
0
15 Oct 2021
Identifying and Mitigating Spurious Correlations for Improving
  Robustness in NLP Models
Identifying and Mitigating Spurious Correlations for Improving Robustness in NLP Models
Tianlu Wang
Rohit Sridhar
Diyi Yang
Xuezhi Wang
AAML
120
72
0
14 Oct 2021
Bugs in our Pockets: The Risks of Client-Side Scanning
Bugs in our Pockets: The Risks of Client-Side Scanning
H. Abelson
Ross J. Anderson
S. Bellovin
Josh Benaloh
M. Blaze
...
Ronald L. Rivest
J. Schiller
B. Schneier
Vanessa J. Teague
Carmela Troncoso
69
39
0
14 Oct 2021
Label Noise in Adversarial Training: A Novel Perspective to Study Robust
  Overfitting
Label Noise in Adversarial Training: A Novel Perspective to Study Robust Overfitting
Chengyu Dong
Liyuan Liu
Jingbo Shang
NoLa
AAML
69
18
0
07 Oct 2021
Trustworthy AI: From Principles to Practices
Trustworthy AI: From Principles to Practices
Bo-wen Li
Peng Qi
Bo Liu
Shuai Di
Jingen Liu
Jiquan Pei
Jinfeng Yi
Bowen Zhou
119
357
0
04 Oct 2021
Trustworthy AI and Robotics and the Implications for the AEC Industry: A
  Systematic Literature Review and Future Potentials
Trustworthy AI and Robotics and the Implications for the AEC Industry: A Systematic Literature Review and Future Potentials
Newsha Emaminejad
Reza Akhavian
28
48
0
27 Sep 2021
Beyond Robustness: A Taxonomy of Approaches towards Resilient
  Multi-Robot Systems
Beyond Robustness: A Taxonomy of Approaches towards Resilient Multi-Robot Systems
Amanda Prorok
Matthew Malencia
Luca Carlone
Gaurav Sukhatme
Brian M. Sadler
Vijay Kumar
101
53
0
25 Sep 2021
Adversarial Robustness for Unsupervised Domain Adaptation
Adversarial Robustness for Unsupervised Domain Adaptation
Muhammad Awais
Fengwei Zhou
Hang Xu
Lanqing Hong
Ping Luo
Sung-Ho Bae
Zhenguo Li
28
39
0
02 Sep 2021
Are socially-aware trajectory prediction models really socially-aware?
Are socially-aware trajectory prediction models really socially-aware?
Saeed Saadatnejad
Mohammadhossein Bahari
Pedram J. Khorsandi
Mohammad Saneian
Seyed-Mohsen Moosavi-Dezfooli
Alexandre Alahi
AAML
32
42
0
24 Aug 2021
Exploring Transferable and Robust Adversarial Perturbation Generation
  from the Perspective of Network Hierarchy
Exploring Transferable and Robust Adversarial Perturbation Generation from the Perspective of Network Hierarchy
Ruikui Wang
Yuanfang Guo
Ruijie Yang
Yunhong Wang
AAML
17
3
0
16 Aug 2021
Neural Architecture Dilation for Adversarial Robustness
Neural Architecture Dilation for Adversarial Robustness
Yanxi Li
Zhaohui Yang
Yunhe Wang
Chang Xu
AAML
38
23
0
16 Aug 2021
Improving the trustworthiness of image classification models by
  utilizing bounding-box annotations
Improving the trustworthiness of image classification models by utilizing bounding-box annotations
K. Dharma
Chicheng Zhang
32
5
0
15 Aug 2021
Triggering Failures: Out-Of-Distribution detection by learning from
  local adversarial attacks in Semantic Segmentation
Triggering Failures: Out-Of-Distribution detection by learning from local adversarial attacks in Semantic Segmentation
Victor Besnier
Andrei Bursuc
David Picard
Alexandre Briot
UQCV
24
48
0
03 Aug 2021
AdvRush: Searching for Adversarially Robust Neural Architectures
AdvRush: Searching for Adversarially Robust Neural Architectures
J. Mok
Byunggook Na
Hyeokjun Choe
Sungroh Yoon
OOD
AAML
22
44
0
03 Aug 2021
Advances in adversarial attacks and defenses in computer vision: A
  survey
Advances in adversarial attacks and defenses in computer vision: A survey
Naveed Akhtar
Ajmal Mian
Navid Kardan
M. Shah
AAML
41
236
0
01 Aug 2021
Detecting Adversarial Examples Is (Nearly) As Hard As Classifying Them
Detecting Adversarial Examples Is (Nearly) As Hard As Classifying Them
Florian Tramèr
AAML
30
65
0
24 Jul 2021
Built-in Elastic Transformations for Improved Robustness
Built-in Elastic Transformations for Improved Robustness
Sadaf Gulshad
Ivan Sosnovik
A. Smeulders
AAML
22
1
0
20 Jul 2021
Trustworthy AI: A Computational Perspective
Trustworthy AI: A Computational Perspective
Haochen Liu
Yiqi Wang
Wenqi Fan
Xiaorui Liu
Yaxin Li
Shaili Jain
Yunhao Liu
Anil K. Jain
Jiliang Tang
FaML
104
197
0
12 Jul 2021
OpenCoS: Contrastive Semi-supervised Learning for Handling Open-set
  Unlabeled Data
OpenCoS: Contrastive Semi-supervised Learning for Handling Open-set Unlabeled Data
Jongjin Park
Sukmin Yun
Jongheon Jeong
Jinwoo Shin
31
29
0
29 Jun 2021
Adversarial Robustness of Streaming Algorithms through Importance
  Sampling
Adversarial Robustness of Streaming Algorithms through Importance Sampling
Vladimir Braverman
Avinatan Hassidim
Yossi Matias
Mariano Schain
Sandeep Silwal
Samson Zhou
AAML
OOD
24
38
0
28 Jun 2021
Data Poisoning Won't Save You From Facial Recognition
Data Poisoning Won't Save You From Facial Recognition
Evani Radiya-Dixit
Sanghyun Hong
Nicholas Carlini
Florian Tramèr
AAML
PICV
22
57
0
28 Jun 2021
How Well do Feature Visualizations Support Causal Understanding of CNN
  Activations?
How Well do Feature Visualizations Support Causal Understanding of CNN Activations?
Roland S. Zimmermann
Judy Borowski
Robert Geirhos
Matthias Bethge
Thomas S. A. Wallis
Wieland Brendel
FAtt
47
31
0
23 Jun 2021
Can contrastive learning avoid shortcut solutions?
Can contrastive learning avoid shortcut solutions?
Joshua Robinson
Li Sun
Ke Yu
Kayhan Batmanghelich
Stefanie Jegelka
S. Sra
SSL
24
143
0
21 Jun 2021
Attack to Fool and Explain Deep Networks
Attack to Fool and Explain Deep Networks
Naveed Akhtar
M. Jalwana
Bennamoun
Ajmal Mian
AAML
32
33
0
20 Jun 2021
Adversarial Visual Robustness by Causal Intervention
Adversarial Visual Robustness by Causal Intervention
Kaihua Tang
Ming Tao
Hanwang Zhang
CML
AAML
32
21
0
17 Jun 2021
Taxonomy of Machine Learning Safety: A Survey and Primer
Taxonomy of Machine Learning Safety: A Survey and Primer
Sina Mohseni
Haotao Wang
Zhiding Yu
Chaowei Xiao
Zhangyang Wang
J. Yadawa
31
31
0
09 Jun 2021
Provably Robust Detection of Out-of-distribution Data (almost) for free
Provably Robust Detection of Out-of-distribution Data (almost) for free
Alexander Meinke
Julian Bitterwolf
Matthias Hein
OODD
33
22
0
08 Jun 2021
Can Subnetwork Structure be the Key to Out-of-Distribution
  Generalization?
Can Subnetwork Structure be the Key to Out-of-Distribution Generalization?
Dinghuai Zhang
Kartik Ahuja
Yilun Xu
Yisen Wang
Aaron Courville
OOD
24
95
0
05 Jun 2021
A Little Robustness Goes a Long Way: Leveraging Robust Features for
  Targeted Transfer Attacks
A Little Robustness Goes a Long Way: Leveraging Robust Features for Targeted Transfer Attacks
Jacob Mitchell Springer
Melanie Mitchell
Garrett Kenyon
AAML
31
43
0
03 Jun 2021
NoiLIn: Improving Adversarial Training and Correcting Stereotype of
  Noisy Labels
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
23
9
0
31 May 2021
Stochastic-Shield: A Probabilistic Approach Towards Training-Free
  Adversarial Defense in Quantized CNNs
Stochastic-Shield: A Probabilistic Approach Towards Training-Free Adversarial Defense in Quantized CNNs
Lorena Qendro
Sangwon Ha
R. D. Jong
Partha P. Maji
AAML
FedML
MQ
21
7
0
13 May 2021
Leveraging Sparse Linear Layers for Debuggable Deep Networks
Leveraging Sparse Linear Layers for Debuggable Deep Networks
Eric Wong
Shibani Santurkar
A. Madry
FAtt
22
88
0
11 May 2021
This Looks Like That... Does it? Shortcomings of Latent Space Prototype
  Interpretability in Deep Networks
This Looks Like That... Does it? Shortcomings of Latent Space Prototype Interpretability in Deep Networks
Adrian Hoffmann
Claudio Fanconi
Rahul Rade
Jonas Köhler
22
63
0
05 May 2021
Impact of Spatial Frequency Based Constraints on Adversarial Robustness
Impact of Spatial Frequency Based Constraints on Adversarial Robustness
Rémi Bernhard
Pierre-Alain Moëllic
Martial Mermillod
Yannick Bourrier
Romain Cohendet
M. Solinas
M. Reyboz
AAML
30
17
0
26 Apr 2021
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