Communities
Connect sessions
AI calendar
Organizations
Join Slack
Contact Sales
Search
Open menu
Home
Papers
2006.00894
Cited By
v1
v2 (latest)
Reducing DNN Labelling Cost using Surprise Adequacy: An Industrial Case Study for Autonomous Driving
29 May 2020
Jinhan Kim
Jeongil Ju
R. Feldt
S. Yoo
Re-assign community
ArXiv (abs)
PDF
HTML
Papers citing
"Reducing DNN Labelling Cost using Surprise Adequacy: An Industrial Case Study for Autonomous Driving"
18 / 18 papers shown
TopoMap: A Feature-based Semantic Discriminator of the Topographical Regions in the Test Input Space
Gianmarco De Vita
Nargiz Humbatova
Paolo Tonella
AAML
OOD
177
0
0
03 Sep 2025
GAN-enhanced Simulation-driven DNN Testing in Absence of Ground Truth
M. Attaoui
F. Pastore
251
1
0
20 Mar 2025
DANDI: Diffusion as Normative Distribution for Deep Neural Network Input
Workshop on Deep Learning for Testing and Testing for Deep Learning (LTTDL), 2025
Somin Kim
Shin Yoo
373
2
0
05 Feb 2025
A Unified Approach Towards Active Learning and Out-of-Distribution Detection
Sebastian Schmidt
Leonard Schenk
Leo Schwinn
Stephan Günnemann
540
6
0
18 May 2024
Synthetic Datasets for Autonomous Driving: A Survey
IEEE Transactions on Intelligent Vehicles (TIV), 2023
Zhihang Song
Zimin He
Xingyu Li
Q. Ma
RuiBo Ming
...
Huaxin Pei
Lihui Peng
Jianming Hu
Dingyi Yao
Yan Zhang
329
99
0
24 Apr 2023
Adopting Two Supervisors for Efficient Use of Large-Scale Remote Deep Neural Networks
ACM Transactions on Software Engineering and Methodology (TOSEM), 2023
Michael Weiss
Paolo Tonella
AI4CE
230
2
0
05 Apr 2023
When and Why Test Generators for Deep Learning Produce Invalid Inputs: an Empirical Study
International Conference on Software Engineering (ICSE), 2022
Vincenzo Riccio
Paolo Tonella
AAML
250
38
0
21 Dec 2022
Anomaly Detection in Driving by Cluster Analysis Twice
Chung-Hao Lee
Yen-Fu Chen
184
0
0
15 Dec 2022
Uncertainty Quantification for Deep Neural Networks: An Empirical Comparison and Usage Guidelines
Software testing, verification & reliability (STVR), 2022
Michael Weiss
Paolo Tonella
BDL
UQCV
218
14
0
14 Dec 2022
Generating and Detecting True Ambiguity: A Forgotten Danger in DNN Supervision Testing
Empirical Software Engineering (EMSE), 2022
Michael Weiss
A. Gómez
Paolo Tonella
AAML
251
9
0
21 Jul 2022
Guiding the retraining of convolutional neural networks against adversarial inputs
PeerJ Computer Science (PeerJ CS), 2022
Francisco Durán
Luís Cruz
Michael Felderer
Xavier Franch
AAML
361
1
0
08 Jul 2022
Simple Techniques Work Surprisingly Well for Neural Network Test Prioritization and Active Learning (Replicability Study)
International Symposium on Software Testing and Analysis (ISSTA), 2022
Michael Weiss
Paolo Tonella
AAML
328
64
0
02 May 2022
Exploring ML testing in practice -- Lessons learned from an interactive rapid review with Axis Communications
Qunying Song
Markus Borg
Emelie Engström
H. Ardö
Sergio Rico
191
10
0
30 Mar 2022
Black-box Safety Analysis and Retraining of DNNs based on Feature Extraction and Clustering
ACM Transactions on Software Engineering and Methodology (TOSEM), 2022
M. Attaoui
Hazem M. Fahmy
F. Pastore
Lionel C. Briand
AAML
401
26
0
13 Jan 2022
MDPFuzz: Testing Models Solving Markov Decision Processes
International Symposium on Software Testing and Analysis (ISSTA), 2021
Qi Pang
Yuanyuan Yuan
Shuai Wang
483
42
0
06 Dec 2021
Revisiting Neuron Coverage for DNN Testing: A Layer-Wise and Distribution-Aware Criterion
Yuanyuan Yuan
Qi Pang
Shuai Wang
281
39
0
03 Dec 2021
A Review and Refinement of Surprise Adequacy
Workshop on Deep Learning for Testing and Testing for Deep Learning (LTTDL), 2021
Michael Weiss
Rwiddhi Chakraborty
Paolo Tonella
AAML
AI4TS
225
18
0
10 Mar 2021
Corner case data description and detection
Workshop on AI Engineering - Software Engineering for AI (ESEA), 2021
Tinghui Ouyang
Vicent Sant Marco
Yoshinao Isobe
H. Asoh
Y. Oiwa
Yoshiki Seo
AAML
277
15
0
07 Jan 2021
1
Page 1 of 1
