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Natural Adversarial Examples
v1v2v3v4 (latest)

Natural Adversarial Examples

Computer Vision and Pattern Recognition (CVPR), 2019
16 July 2019
Dan Hendrycks
Kevin Zhao
Steven Basart
Jacob Steinhardt
Basel Alomair
    OODD
ArXiv (abs)PDFHTML

Papers citing "Natural Adversarial Examples"

50 / 1,169 papers shown
Accuracy on the Line: On the Strong Correlation Between
  Out-of-Distribution and In-Distribution Generalization
Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution GeneralizationInternational Conference on Machine Learning (ICML), 2021
John Miller
Rohan Taori
Aditi Raghunathan
Shiori Sagawa
Pang Wei Koh
Vaishaal Shankar
Abigail Z. Jacobs
Y. Carmon
Ludwig Schmidt
OODDOOD
378
313
0
09 Jul 2021
Predicting with Confidence on Unseen Distributions
Predicting with Confidence on Unseen Distributions
Devin Guillory
Vaishaal Shankar
Sayna Ebrahimi
Trevor Darrell
Ludwig Schmidt
UQCVOOD
292
138
0
07 Jul 2021
Long-Short Transformer: Efficient Transformers for Language and Vision
Long-Short Transformer: Efficient Transformers for Language and Vision
Chen Zhu
Ming-Yu Liu
Chaowei Xiao
Mohammad Shoeybi
Tom Goldstein
Anima Anandkumar
Bryan Catanzaro
ViTVLM
439
161
0
05 Jul 2021
Global Filter Networks for Image Classification
Global Filter Networks for Image Classification
Yongming Rao
Wenliang Zhao
Zheng Zhu
Jiwen Lu
Jie Zhou
ViT
300
605
0
01 Jul 2021
Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity
Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic SparsityInternational Conference on Learning Representations (ICLR), 2021
Shiwei Liu
Tianlong Chen
Zahra Atashgahi
Xiaohan Chen
Ghada Sokar
Elena Mocanu
Mykola Pechenizkiy
Zinan Lin
Decebal Constantin Mocanu
OOD
387
62
0
28 Jun 2021
Explainable AI for Natural Adversarial Images
Explainable AI for Natural Adversarial Images
Tomas Folke
Zhaobin Li
Ravi B. Sojitra
Scott Cheng-Hsin Yang
Patrick Shafto
AAMLFAtt
111
4
0
16 Jun 2021
A Framework for Evaluating Post Hoc Feature-Additive Explainers
A Framework for Evaluating Post Hoc Feature-Additive Explainers
Zachariah Carmichael
Walter J. Scheirer
FAtt
196
5
0
15 Jun 2021
Revisiting the Calibration of Modern Neural Networks
Revisiting the Calibration of Modern Neural Networks
Matthias Minderer
Josip Djolonga
Rob Romijnders
F. Hubis
Xiaohua Zhai
N. Houlsby
Dustin Tran
Mario Lucic
UQCV
410
447
0
15 Jun 2021
Partial success in closing the gap between human and machine vision
Partial success in closing the gap between human and machine visionNeural Information Processing Systems (NeurIPS), 2021
Robert Geirhos
Kantharaju Narayanappa
Benjamin Mitzkus
Tizian Thieringer
Matthias Bethge
Felix Wichmann
Wieland Brendel
VLMAAML
325
267
0
14 Jun 2021
Transformed CNNs: recasting pre-trained convolutional layers with
  self-attention
Transformed CNNs: recasting pre-trained convolutional layers with self-attention
Stéphane dÁscoli
Levent Sagun
Giulio Biroli
Ari S. Morcos
ViT
106
7
0
10 Jun 2021
Taxonomy of Machine Learning Safety: A Survey and Primer
Taxonomy of Machine Learning Safety: A Survey and PrimerACM Computing Surveys (CSUR), 2021
Sina Mohseni
Haotao Wang
Zhiding Yu
Chaowei Xiao
Zinan Lin
J. Yadawa
314
47
0
09 Jun 2021
White Paper Assistance: A Step Forward Beyond the Shortcut Learning
White Paper Assistance: A Step Forward Beyond the Shortcut Learning
Xuan Cheng
Tianshu Xie
Xiaomin Wang
Jiali Deng
Minghui Liu
Meilin Liu
AAML
209
0
0
08 Jun 2021
3DB: A Framework for Debugging Computer Vision Models
3DB: A Framework for Debugging Computer Vision ModelsNeural Information Processing Systems (NeurIPS), 2021
Guillaume Leclerc
Hadi Salman
Andrew Ilyas
Sai H. Vemprala
Logan Engstrom
...
Pengchuan Zhang
Shibani Santurkar
Greg Yang
Ashish Kapoor
Aleksander Madry
244
44
0
07 Jun 2021
OoD-Bench: Quantifying and Understanding Two Dimensions of
  Out-of-Distribution Generalization
OoD-Bench: Quantifying and Understanding Two Dimensions of Out-of-Distribution GeneralizationComputer Vision and Pattern Recognition (CVPR), 2021
Nanyang Ye
Kaican Li
Haoyue Bai
Runpeng Yu
Lanqing Hong
Fengwei Zhou
Zhenguo Li
Jun Zhu
CMLOOD
289
130
0
07 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 AttacksNeural Information Processing Systems (NeurIPS), 2021
Jacob Mitchell Springer
Melanie Mitchell
Garrett Kenyon
AAML
288
49
0
03 Jun 2021
Personalizing Pre-trained Models
Personalizing Pre-trained Models
Mina Khan
P. Srivatsa
Advait Rane
Shriram Chenniappa
A. Hazariwala
Pattie Maes
VLM
204
7
0
02 Jun 2021
Exposing Previously Undetectable Faults in Deep Neural Networks
Exposing Previously Undetectable Faults in Deep Neural NetworksInternational Symposium on Software Testing and Analysis (ISSTA), 2021
Isaac Dunn
Hadrien Pouget
Daniel Kroening
T. Melham
AAML
217
31
0
01 Jun 2021
The effectiveness of feature attribution methods and its correlation
  with automatic evaluation scores
The effectiveness of feature attribution methods and its correlation with automatic evaluation scoresNeural Information Processing Systems (NeurIPS), 2021
Giang Nguyen
Daeyoung Kim
Anh Totti Nguyen
FAtt
514
105
0
31 May 2021
Towards Robust Vision Transformer
Towards Robust Vision TransformerComputer Vision and Pattern Recognition (CVPR), 2021
Xiaofeng Mao
Gege Qi
YueFeng Chen
Xiaodan Li
Ranjie Duan
Shaokai Ye
Yuan He
Hui Xue
ViT
434
233
0
17 May 2021
Vision Transformers are Robust Learners
Vision Transformers are Robust LearnersAAAI Conference on Artificial Intelligence (AAAI), 2021
Sayak Paul
Pin-Yu Chen
ViT
360
352
0
17 May 2021
Scaling Ensemble Distribution Distillation to Many Classes with Proxy
  Targets
Scaling Ensemble Distribution Distillation to Many Classes with Proxy TargetsNeural Information Processing Systems (NeurIPS), 2021
Max Ryabinin
A. Malinin
Mark Gales
UQCV
187
20
0
14 May 2021
RC2020 Report: Learning De-biased Representations with Biased
  Representations
RC2020 Report: Learning De-biased Representations with Biased Representations
Rwiddhi Chakraborty
Shubhayu Das
60
0
0
14 May 2021
If your data distribution shifts, use self-learning
If your data distribution shifts, use self-learning
E. Rusak
Steffen Schneider
George Pachitariu
L. Eck
Peter V. Gehler
Oliver Bringmann
Wieland Brendel
Matthias Bethge
VLMOODTTA
430
36
0
27 Apr 2021
SoT: Delving Deeper into Classification Head for Transformer
SoT: Delving Deeper into Classification Head for Transformer
Jiangtao Xie
Rui Zeng
Qilong Wang
Ziqi Zhou
P. Li
ViT
232
12
0
22 Apr 2021
Patch Shortcuts: Interpretable Proxy Models Efficiently Find Black-Box
  Vulnerabilities
Patch Shortcuts: Interpretable Proxy Models Efficiently Find Black-Box Vulnerabilities
Julia Rosenzweig
Joachim Sicking
Sebastian Houben
Michael Mock
Maram Akila
AAML
279
3
0
22 Apr 2021
Towards Corruption-Agnostic Robust Domain Adaptation
Towards Corruption-Agnostic Robust Domain Adaptation
Yifan Xu
Kekai Sheng
Weiming Dong
Baoyuan Wu
Changsheng Xu
Bao-Gang Hu
286
9
0
21 Apr 2021
Provable Repair of Deep Neural Networks
Provable Repair of Deep Neural NetworksACM-SIGPLAN Symposium on Programming Language Design and Implementation (PLDI), 2021
Matthew Sotoudeh
Aditya V. Thakur
AAML
301
81
0
09 Apr 2021
Defending Against Image Corruptions Through Adversarial Augmentations
Defending Against Image Corruptions Through Adversarial AugmentationsInternational Conference on Learning Representations (ICLR), 2021
D. A. Calian
Florian Stimberg
Olivia Wiles
Sylvestre-Alvise Rebuffi
András Gyorgy
Timothy A. Mann
Sven Gowal
AAML
335
46
0
02 Apr 2021
Rethinking Spatial Dimensions of Vision Transformers
Rethinking Spatial Dimensions of Vision TransformersIEEE International Conference on Computer Vision (ICCV), 2021
Byeongho Heo
Sangdoo Yun
Dongyoon Han
Sanghyuk Chun
Junsuk Choe
Seong Joon Oh
ViT
1.5K
693
0
30 Mar 2021
Towards Understanding Adversarial Robustness of Optical Flow Networks
Towards Understanding Adversarial Robustness of Optical Flow NetworksComputer Vision and Pattern Recognition (CVPR), 2021
Simon Schrodi
Tonmoy Saikia
Thomas Brox
AAML
190
18
0
30 Mar 2021
Understanding Robustness of Transformers for Image Classification
Understanding Robustness of Transformers for Image ClassificationIEEE International Conference on Computer Vision (ICCV), 2021
Srinadh Bhojanapalli
Ayan Chakrabarti
Daniel Glasner
Daliang Li
Thomas Unterthiner
Andreas Veit
ViT
313
472
0
26 Mar 2021
StyleLess layer: Improving robustness for real-world driving
StyleLess layer: Improving robustness for real-world drivingIEEE/RJS International Conference on Intelligent RObots and Systems (IROS), 2021
Julien Rebut
Andrei Bursuc
P. Pérez
214
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0
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A Broad Study on the Transferability of Visual Representations with
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A Broad Study on the Transferability of Visual Representations with Contrastive LearningIEEE International Conference on Computer Vision (ICCV), 2021
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Chun-Fu Chen
Yikang Shen
Leonid Karlinsky
Richard J. Radke
Rogerio Feris
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325
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0
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Characterizing and Improving the Robustness of Self-Supervised Learning
  through Background Augmentations
Characterizing and Improving the Robustness of Self-Supervised Learning through Background Augmentations
Chaitanya K. Ryali
D. Schwab
Ari S. Morcos
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257
9
0
23 Mar 2021
Measuring and modeling the motor system with machine learning
Measuring and modeling the motor system with machine learningCurrent Opinion in Neurobiology (Curr Opin Neurobiol), 2021
Sébastien B Hausmann
Alessandro Marin Vargas
Alexander Mathis
Mackenzie W. Mathis
204
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0
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Adversarially Optimized Mixup for Robust Classification
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Jason Bunk
Srinjoy Chattopadhyay
B. S. Manjunath
S. Chandrasekaran
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212
9
0
22 Mar 2021
Limitations of Post-Hoc Feature Alignment for Robustness
Limitations of Post-Hoc Feature Alignment for RobustnessComputer Vision and Pattern Recognition (CVPR), 2021
Collin Burns
Jacob Steinhardt
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197
23
0
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Learning ABCs: Approximate Bijective Correspondence for isolating
  factors of variation with weak supervision
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Varun Jampani
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Domain Generalization: A Survey
Domain Generalization: A SurveyIEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021
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Chen Change Loy
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840
1,336
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On the effectiveness of adversarial training against common corruptions
On the effectiveness of adversarial training against common corruptionsConference on Uncertainty in Artificial Intelligence (UAI), 2021
Klim Kireev
Maksym Andriushchenko
Nicolas Flammarion
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331
112
0
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Adversarial Reciprocal Points Learning for Open Set Recognition
Adversarial Reciprocal Points Learning for Open Set RecognitionIEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021
Guangyao Chen
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Xiangqian Wang
Yonghong Tian
590
394
0
01 Mar 2021
Exposing Semantic Segmentation Failures via Maximum Discrepancy
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Yu Zhong
Yuming Fang
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Kede Ma
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232
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Learning Transferable Visual Models From Natural Language Supervision
Learning Transferable Visual Models From Natural Language SupervisionInternational Conference on Machine Learning (ICML), 2021
Alec Radford
Jong Wook Kim
Chris Hallacy
Aditya A. Ramesh
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Pamela Mishkin
Jack Clark
Gretchen Krueger
Ilya Sutskever
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Rethinking Natural Adversarial Examples for Classification Models
Rethinking Natural Adversarial Examples for Classification Models
Xiao-Li Li
Jianmin Li
Ting Dai
Jie Shi
Jun Zhu
Xiaolin Hu
AAMLVLM
224
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Deep Deterministic Uncertainty: A Simple Baseline
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463
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A Too-Good-to-be-True Prior to Reduce Shortcut Reliance
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Scaling Up Visual and Vision-Language Representation Learning With Noisy
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