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There is Limited Correlation between Coverage and Robustness for Deep
  Neural Networks

There is Limited Correlation between Coverage and Robustness for Deep Neural Networks

14 November 2019
Yizhen Dong
Peixin Zhang
Jingyi Wang
Shuang Liu
Jun Sun
Jianye Hao
Xinyu Wang
Li Wang
J. Dong
Ting Dai
    OOD
    AAML
ArXivPDFHTML

Papers citing "There is Limited Correlation between Coverage and Robustness for Deep Neural Networks"

33 / 33 papers shown
Title
Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural
  Networks
Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks
Mahyar Fazlyab
Alexander Robey
Hamed Hassani
M. Morari
George J. Pappas
87
456
0
12 Jun 2019
Adversarial Sample Detection for Deep Neural Network through Model
  Mutation Testing
Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing
Jingyi Wang
Guoliang Dong
Jun Sun
Xinyu Wang
Peixin Zhang
AAML
41
191
0
14 Dec 2018
BERT: Pre-training of Deep Bidirectional Transformers for Language
  Understanding
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin
Ming-Wei Chang
Kenton Lee
Kristina Toutanova
VLM
SSL
SSeg
1.4K
94,511
0
11 Oct 2018
Adversarial Examples: Opportunities and Challenges
Adversarial Examples: Opportunities and Challenges
Jiliang Zhang
Chen Li
AAML
53
233
0
13 Sep 2018
Guiding Deep Learning System Testing using Surprise Adequacy
Guiding Deep Learning System Testing using Surprise Adequacy
Jinhan Kim
R. Feldt
S. Yoo
AAML
ELM
64
431
0
25 Aug 2018
TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing
TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing
Augustus Odena
Ian Goodfellow
AAML
57
321
0
28 Jul 2018
Robustness May Be at Odds with Accuracy
Robustness May Be at Odds with Accuracy
Dimitris Tsipras
Shibani Santurkar
Logan Engstrom
Alexander Turner
Aleksander Madry
AAML
93
1,776
0
30 May 2018
Lipschitz regularity of deep neural networks: analysis and efficient
  estimation
Lipschitz regularity of deep neural networks: analysis and efficient estimation
Kevin Scaman
Aladin Virmaux
75
527
0
28 May 2018
Concolic Testing for Deep Neural Networks
Concolic Testing for Deep Neural Networks
Youcheng Sun
Min Wu
Wenjie Ruan
Xiaowei Huang
Marta Kwiatkowska
Daniel Kroening
52
334
0
30 Apr 2018
DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems
DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems
Lei Ma
Felix Juefei Xu
Fuyuan Zhang
Jiyuan Sun
Minhui Xue
...
Ting Su
Li Li
Yang Liu
Jianjun Zhao
Yadong Wang
ELM
65
620
0
20 Mar 2018
Testing Deep Neural Networks
Testing Deep Neural Networks
Youcheng Sun
Xiaowei Huang
Daniel Kroening
James Sharp
Matthew Hill
Rob Ashmore
AAML
52
216
0
10 Mar 2018
Evaluating the Robustness of Neural Networks: An Extreme Value Theory
  Approach
Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach
Tsui-Wei Weng
Huan Zhang
Pin-Yu Chen
Jinfeng Yi
D. Su
Yupeng Gao
Cho-Jui Hsieh
Luca Daniel
AAML
76
467
0
31 Jan 2018
Towards Imperceptible and Robust Adversarial Example Attacks against
  Neural Networks
Towards Imperceptible and Robust Adversarial Example Attacks against Neural Networks
Bo Luo
Yannan Liu
Lingxiao Wei
Q. Xu
AAML
51
142
0
15 Jan 2018
DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous
  Cars
DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars
Yuchi Tian
Kexin Pei
Suman Jana
Baishakhi Ray
AAML
59
1,356
0
28 Aug 2017
Towards Deep Learning Models Resistant to Adversarial Attacks
Towards Deep Learning Models Resistant to Adversarial Attacks
Aleksander Madry
Aleksandar Makelov
Ludwig Schmidt
Dimitris Tsipras
Adrian Vladu
SILM
OOD
265
12,029
0
19 Jun 2017
Formal Guarantees on the Robustness of a Classifier against Adversarial
  Manipulation
Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation
Matthias Hein
Maksym Andriushchenko
AAML
83
511
0
23 May 2017
DeepXplore: Automated Whitebox Testing of Deep Learning Systems
DeepXplore: Automated Whitebox Testing of Deep Learning Systems
Kexin Pei
Yinzhi Cao
Junfeng Yang
Suman Jana
AAML
82
1,361
0
18 May 2017
Parseval Networks: Improving Robustness to Adversarial Examples
Parseval Networks: Improving Robustness to Adversarial Examples
Moustapha Cissé
Piotr Bojanowski
Edouard Grave
Yann N. Dauphin
Nicolas Usunier
AAML
136
806
0
28 Apr 2017
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Guy Katz
Clark W. Barrett
D. Dill
Kyle D. Julian
Mykel Kochenderfer
AAML
299
1,860
0
03 Feb 2017
Adversarial Machine Learning at Scale
Adversarial Machine Learning at Scale
Alexey Kurakin
Ian Goodfellow
Samy Bengio
AAML
461
3,138
0
04 Nov 2016
Technical Report on the CleverHans v2.1.0 Adversarial Examples Library
Technical Report on the CleverHans v2.1.0 Adversarial Examples Library
Nicolas Papernot
Fartash Faghri
Nicholas Carlini
Ian Goodfellow
Reuben Feinman
...
David Berthelot
P. Hendricks
Jonas Rauber
Rujun Long
Patrick McDaniel
AAML
56
512
0
03 Oct 2016
Towards Evaluating the Robustness of Neural Networks
Towards Evaluating the Robustness of Neural Networks
Nicholas Carlini
D. Wagner
OOD
AAML
222
8,533
0
16 Aug 2016
Adversarial examples in the physical world
Adversarial examples in the physical world
Alexey Kurakin
Ian Goodfellow
Samy Bengio
SILM
AAML
517
5,885
0
08 Jul 2016
TensorFlow: A system for large-scale machine learning
TensorFlow: A system for large-scale machine learning
Martín Abadi
P. Barham
Jianmin Chen
Zhiwen Chen
Andy Davis
...
Vijay Vasudevan
Pete Warden
Martin Wicke
Yuan Yu
Xiaoqiang Zhang
GNN
AI4CE
398
18,334
0
27 May 2016
End to End Learning for Self-Driving Cars
End to End Learning for Self-Driving Cars
Mariusz Bojarski
D. Testa
Daniel Dworakowski
Bernhard Firner
B. Flepp
...
Urs Muller
Jiakai Zhang
Xin Zhang
Jake Zhao
Karol Zieba
SSL
73
4,163
0
25 Apr 2016
Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
1.9K
193,426
0
10 Dec 2015
The Limitations of Deep Learning in Adversarial Settings
The Limitations of Deep Learning in Adversarial Settings
Nicolas Papernot
Patrick McDaniel
S. Jha
Matt Fredrikson
Z. Berkay Celik
A. Swami
AAML
76
3,955
0
24 Nov 2015
FaceNet: A Unified Embedding for Face Recognition and Clustering
FaceNet: A Unified Embedding for Face Recognition and Clustering
Florian Schroff
Dmitry Kalenichenko
James Philbin
3DH
330
13,123
0
12 Mar 2015
Explaining and Harnessing Adversarial Examples
Explaining and Harnessing Adversarial Examples
Ian Goodfellow
Jonathon Shlens
Christian Szegedy
AAML
GAN
229
19,017
0
20 Dec 2014
Going Deeper with Convolutions
Going Deeper with Convolutions
Christian Szegedy
Wei Liu
Yangqing Jia
P. Sermanet
Scott E. Reed
Dragomir Anguelov
D. Erhan
Vincent Vanhoucke
Andrew Rabinovich
397
43,589
0
17 Sep 2014
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan
Andrew Zisserman
FAtt
MDE
1.3K
100,213
0
04 Sep 2014
Intriguing properties of neural networks
Intriguing properties of neural networks
Christian Szegedy
Wojciech Zaremba
Ilya Sutskever
Joan Bruna
D. Erhan
Ian Goodfellow
Rob Fergus
AAML
235
14,893
1
21 Dec 2013
Robustness and Generalization
Robustness and Generalization
Huan Xu
Shie Mannor
OOD
180
460
0
13 May 2010
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