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Generating and Detecting True Ambiguity: A Forgotten Danger in DNN
  Supervision Testing
v1v2 (latest)

Generating and Detecting True Ambiguity: A Forgotten Danger in DNN Supervision Testing

21 July 2022
Michael Weiss
A. Gómez
Paolo Tonella
    AAML
ArXiv (abs)PDFHTML

Papers citing "Generating and Detecting True Ambiguity: A Forgotten Danger in DNN Supervision Testing"

42 / 42 papers shown
Title
Uncertainty Quantification for Deep Neural Networks: An Empirical
  Comparison and Usage Guidelines
Uncertainty Quantification for Deep Neural Networks: An Empirical Comparison and Usage Guidelines
Michael Weiss
Paolo Tonella
BDLUQCV
57
11
0
14 Dec 2022
Simple Techniques Work Surprisingly Well for Neural Network Test
  Prioritization and Active Learning (Replicability Study)
Simple Techniques Work Surprisingly Well for Neural Network Test Prioritization and Active Learning (Replicability Study)
Michael Weiss
Paolo Tonella
AAML
54
51
0
02 May 2022
DeepGuard: A Framework for Safeguarding Autonomous Driving Systems from
  Inconsistent Behavior
DeepGuard: A Framework for Safeguarding Autonomous Driving Systems from Inconsistent Behavior
Manzoor Hussain
Nazakat Ali
Jang-Eui Hong
AAML
31
39
0
18 Nov 2021
Datasets: A Community Library for Natural Language Processing
Datasets: A Community Library for Natural Language Processing
Quentin Lhoest
Albert Villanova del Moral
Yacine Jernite
A. Thakur
Patrick von Platen
...
Thibault Goehringer
Victor Mustar
François Lagunas
Alexander M. Rush
Thomas Wolf
216
610
0
07 Sep 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
68
240
0
01 Aug 2021
Prediction Surface Uncertainty Quantification in Object Detection Models
  for Autonomous Driving
Prediction Surface Uncertainty Quantification in Object Detection Models for Autonomous Driving
Ferhat Ozgur Catak
T. Yue
Shaukat Ali
61
22
0
11 Jul 2021
Exposing Previously Undetectable Faults in Deep Neural Networks
Exposing Previously Undetectable Faults in Deep Neural Networks
Isaac Dunn
Hadrien Pouget
Daniel Kroening
T. Melham
AAML
104
29
0
01 Jun 2021
A Review on Oracle Issues in Machine Learning
A Review on Oracle Issues in Machine Learning
Diogo Seca
VLMAAML
44
3
0
04 May 2021
Performance Analysis of Out-of-Distribution Detection on Various Trained
  Neural Networks
Performance Analysis of Out-of-Distribution Detection on Various Trained Neural Networks
Jens Henriksson
C. Berger
Markus Borg
Lars Tornberg
S. Sathyamoorthy
Cristofer Englund
OODD
58
17
0
29 Mar 2021
A Review and Refinement of Surprise Adequacy
A Review and Refinement of Surprise Adequacy
Michael Weiss
Rwiddhi Chakraborty
Paolo Tonella
AAMLAI4TS
43
16
0
10 Mar 2021
Distribution-Aware Testing of Neural Networks Using Generative Models
Distribution-Aware Testing of Neural Networks Using Generative Models
Swaroopa Dola
Matthew B. Dwyer
M. Soffa
71
53
0
26 Feb 2021
On Feature Collapse and Deep Kernel Learning for Single Forward Pass
  Uncertainty
On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty
Joost R. van Amersfoort
Lewis Smith
Andrew Jesson
Oscar Key
Y. Gal
UQCV
69
104
0
22 Feb 2021
Fail-Safe Execution of Deep Learning based Systems through Uncertainty
  Monitoring
Fail-Safe Execution of Deep Learning based Systems through Uncertainty Monitoring
Michael Weiss
Paolo Tonella
AAML
109
30
0
01 Feb 2021
Uncertainty-Wizard: Fast and User-Friendly Neural Network Uncertainty
  Quantification
Uncertainty-Wizard: Fast and User-Friendly Neural Network Uncertainty Quantification
Michael Weiss
Paolo Tonella
UQCV
61
21
0
29 Dec 2020
The Hidden Uncertainty in a Neural Networks Activations
The Hidden Uncertainty in a Neural Networks Activations
Janis Postels
Hermann Blum
Yannick Strümpler
Cesar Cadena
Roland Siegwart
Luc Van Gool
Federico Tombari
UQCV
130
22
0
05 Dec 2020
Model-based Exploration of the Frontier of Behaviours for Deep Learning
  System Testing
Model-based Exploration of the Frontier of Behaviours for Deep Learning System Testing
Vincenzo Riccio
Paolo Tonella
AAML
36
131
0
06 Jul 2020
Reducing DNN Labelling Cost using Surprise Adequacy: An Industrial Case
  Study for Autonomous Driving
Reducing DNN Labelling Cost using Surprise Adequacy: An Industrial Case Study for Autonomous Driving
Jinhan Kim
Jeongil Ju
R. Feldt
S. Yoo
32
47
0
29 May 2020
SINVAD: Search-based Image Space Navigation for DNN Image Classifier
  Test Input Generation
SINVAD: Search-based Image Space Navigation for DNN Image Classifier Test Input Generation
Sungmin Kang
R. Feldt
S. Yoo
AAML
75
32
0
19 May 2020
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
50
76
0
24 Apr 2020
Manifold for Machine Learning Assurance
Manifold for Machine Learning Assurance
Taejoon Byun
Sanjai Rayadurgam
58
30
0
08 Feb 2020
Characterizing the Decision Boundary of Deep Neural Networks
Characterizing the Decision Boundary of Deep Neural Networks
Hamid Karimi
Hanyu Wang
Jiliang Tang
51
67
0
24 Dec 2019
Taxonomy of Real Faults in Deep Learning Systems
Taxonomy of Real Faults in Deep Learning Systems
Nargiz Humbatova
Gunel Jahangirova
Gabriele Bavota
Vincenzo Riccio
Andrea Stocco
Paolo Tonella
92
268
0
24 Oct 2019
MNIST-C: A Robustness Benchmark for Computer Vision
MNIST-C: A Robustness Benchmark for Computer Vision
Norman Mu
Justin Gilmer
54
212
0
05 Jun 2019
Estimating Risk and Uncertainty in Deep Reinforcement Learning
Estimating Risk and Uncertainty in Deep Reinforcement Learning
W. Clements
B. V. Delft
Benoît-Marie Robaglia
Reda Bahi Slaoui
Sébastien Toth
64
97
0
23 May 2019
Benchmarking Neural Network Robustness to Common Corruptions and
  Perturbations
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
Dan Hendrycks
Thomas G. Dietterich
OODVLM
172
3,435
0
28 Mar 2019
Towards Structured Evaluation of Deep Neural Network Supervisors
Towards Structured Evaluation of Deep Neural Network Supervisors
Jens Henriksson
C. Berger
Markus Borg
Lars Tornberg
Cristofer Englund
S. Sathyamoorthy
Stig Ursing
AAML
34
37
0
04 Mar 2019
Guiding Deep Learning System Testing using Surprise Adequacy
Guiding Deep Learning System Testing using Surprise Adequacy
Jinhan Kim
R. Feldt
S. Yoo
AAMLELM
71
432
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
61
322
0
28 Jul 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
64
1,359
0
28 Aug 2017
Explainable Artificial Intelligence: Understanding, Visualizing and
  Interpreting Deep Learning Models
Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models
Wojciech Samek
Thomas Wiegand
K. Müller
XAIVLM
75
1,190
0
28 Aug 2017
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning
  Algorithms
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
Han Xiao
Kashif Rasul
Roland Vollgraf
283
8,904
0
25 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
SILMOOD
307
12,069
0
19 Jun 2017
Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCVBDL
840
5,821
0
05 Dec 2016
A Baseline for Detecting Misclassified and Out-of-Distribution Examples
  in Neural Networks
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
Dan Hendrycks
Kevin Gimpel
UQCV
158
3,454
0
07 Oct 2016
Densely Connected Convolutional Networks
Densely Connected Convolutional Networks
Gao Huang
Zhuang Liu
Laurens van der Maaten
Kilian Q. Weinberger
PINN3DV
772
36,813
0
25 Aug 2016
Adversarial examples in the physical world
Adversarial examples in the physical world
Alexey Kurakin
Ian Goodfellow
Samy Bengio
SILMAAML
540
5,897
0
08 Jul 2016
Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
2.2K
194,020
0
10 Dec 2015
Adversarial Autoencoders
Adversarial Autoencoders
Alireza Makhzani
Jonathon Shlens
Navdeep Jaitly
Ian Goodfellow
Brendan J. Frey
GAN
86
2,228
0
18 Nov 2015
DeepFool: a simple and accurate method to fool deep neural networks
DeepFool: a simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli
Alhussein Fawzi
P. Frossard
AAML
151
4,897
0
14 Nov 2015
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCVBDL
824
9,318
0
06 Jun 2015
Explaining and Harnessing Adversarial Examples
Explaining and Harnessing Adversarial Examples
Ian Goodfellow
Jonathon Shlens
Christian Szegedy
AAMLGAN
277
19,066
0
20 Dec 2014
Auto-Encoding Variational Bayes
Auto-Encoding Variational Bayes
Diederik P. Kingma
Max Welling
BDL
452
16,929
0
20 Dec 2013
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