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Guiding Deep Learning System Testing using Surprise Adequacy

Guiding Deep Learning System Testing using Surprise Adequacy

25 August 2018
Jinhan Kim
R. Feldt
S. Yoo
    AAML
    ELM
ArXivPDFHTML

Papers citing "Guiding Deep Learning System Testing using Surprise Adequacy"

18 / 68 papers shown
Title
Towards Characterizing Adversarial Defects of Deep Learning Software
  from the Lens of Uncertainty
Towards Characterizing Adversarial Defects of Deep Learning Software from the Lens of Uncertainty
Xiyue Zhang
Xiaofei Xie
Lei Ma
Xiaoning Du
Q. Hu
Yang Liu
Jianjun Zhao
Meng Sun
AAML
16
76
0
24 Apr 2020
Manifold-based Test Generation for Image Classifiers
Manifold-based Test Generation for Image Classifiers
Taejoon Byun
Abhishek Vijayakumar
Sanjai Rayadurgam
D. Cofer
18
9
0
15 Feb 2020
Importance-Driven Deep Learning System Testing
Importance-Driven Deep Learning System Testing
Simos Gerasimou
Hasan Ferit Eniser
A. Sen
Alper Çakan
AAML
VLM
32
98
0
09 Feb 2020
Manifold for Machine Learning Assurance
Manifold for Machine Learning Assurance
Taejoon Byun
Sanjai Rayadurgam
44
29
0
08 Feb 2020
Supporting DNN Safety Analysis and Retraining through Heatmap-based
  Unsupervised Learning
Supporting DNN Safety Analysis and Retraining through Heatmap-based Unsupervised Learning
Hazem M. Fahmy
F. Pastore
M. Bagherzadeh
Lionel C. Briand
AI4CE
AAML
43
30
0
03 Feb 2020
Arachne: Search Based Repair of Deep Neural Networks
Arachne: Search Based Repair of Deep Neural Networks
Jeongju Sohn
Sungmin Kang
S. Yoo
KELM
30
46
0
28 Dec 2019
Metamorphic Testing for Object Detection Systems
Metamorphic Testing for Object Detection Systems
Shuai Wang
Z. Su
34
23
0
19 Dec 2019
DeepSmartFuzzer: Reward Guided Test Generation For Deep Learning
DeepSmartFuzzer: Reward Guided Test Generation For Deep Learning
Samet Demir
Hasan Ferit Eniser
A. Sen
AAML
11
28
0
24 Nov 2019
There is Limited Correlation between Coverage and Robustness for Deep
  Neural Networks
There is Limited Correlation between Coverage and Robustness for Deep Neural Networks
Yizhen Dong
Peixin Zhang
Jingyi Wang
Shuang Liu
Jun Sun
Jianye Hao
Xinyu Wang
Li Wang
J. Dong
Ting Dai
OOD
AAML
21
32
0
14 Nov 2019
Coverage Guided Testing for Recurrent Neural Networks
Coverage Guided Testing for Recurrent Neural Networks
Wei Huang
Youcheng Sun
Xing-E. Zhao
James Sharp
Wenjie Ruan
Jie Meng
Xiaowei Huang
AAML
25
47
0
05 Nov 2019
Structure-Invariant Testing for Machine Translation
Structure-Invariant Testing for Machine Translation
Pinjia He
Clara Meister
Z. Su
27
104
0
19 Jul 2019
Machine Learning Testing: Survey, Landscapes and Horizons
Machine Learning Testing: Survey, Landscapes and Horizons
Jie M. Zhang
Mark Harman
Lei Ma
Yang Liu
VLM
AILaw
39
739
0
19 Jun 2019
Testing DNN Image Classifiers for Confusion & Bias Errors
Testing DNN Image Classifiers for Confusion & Bias Errors
Yuchi Tian
Ziyuan Zhong
Vicente Ordonez
Gail E. Kaiser
Baishakhi Ray
24
52
0
20 May 2019
Test Selection for Deep Learning Systems
Test Selection for Deep Learning Systems
Wei Ma
Mike Papadakis
Anestis Tsakmalis
Maxime Cordy
Yves Le Traon
OOD
21
92
0
30 Apr 2019
Input Prioritization for Testing Neural Networks
Input Prioritization for Testing Neural Networks
Taejoon Byun
Vaibhav Sharma
Abhishek Vijayakumar
Sanjai Rayadurgam
D. Cofer
AAML
29
67
0
11 Jan 2019
Secure Deep Learning Engineering: A Software Quality Assurance
  Perspective
Secure Deep Learning Engineering: A Software Quality Assurance Perspective
Lei Ma
Felix Juefei Xu
Minhui Xue
Q. Hu
Sen Chen
Bo-wen Li
Yang Liu
Jianjun Zhao
Jianxiong Yin
Simon See
AAML
27
35
0
10 Oct 2018
Safety Verification of Deep Neural Networks
Safety Verification of Deep Neural Networks
Xiaowei Huang
Marta Kwiatkowska
Sen Wang
Min Wu
AAML
180
932
0
21 Oct 2016
Adversarial examples in the physical world
Adversarial examples in the physical world
Alexey Kurakin
Ian Goodfellow
Samy Bengio
SILM
AAML
308
5,847
0
08 Jul 2016
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