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

29 May 2020

Papers citing "Reducing DNN Labelling Cost using Surprise Adequacy: An Industrial Case Study for Autonomous Driving"

10 / 10 papers shown

Title
A Unified Approach Towards Active Learning and Out-of-Distribution Detection Sebastian Schmidt Leonard Schenk Leo Schwinn Stephan Günnemann 58 3 0 18 May 2024
When and Why Test Generators for Deep Learning Produce Invalid Inputs: an Empirical Study Vincenzo Riccio Paolo Tonella AAML 24 29 0 21 Dec 2022
Anomaly Detection in Driving by Cluster Analysis Twice Chung-Hao Lee Yen-Fu Chen 19 0 0 15 Dec 2022
Uncertainty Quantification for Deep Neural Networks: An Empirical Comparison and Usage Guidelines Michael Weiss Paolo Tonella BDL UQCV 30 11 0 14 Dec 2022
Generating and Detecting True Ambiguity: A Forgotten Danger in DNN Supervision Testing Michael Weiss A. Gómez Paolo Tonella AAML 18 6 0 21 Jul 2022
Guiding the retraining of convolutional neural networks against adversarial inputs Francisco Durán Silverio Martínez-Fernández Michael Felderer Xavier Franch AAML 43 1 0 08 Jul 2022
Simple Techniques Work Surprisingly Well for Neural Network Test Prioritization and Active Learning (Replicability Study) Michael Weiss Paolo Tonella AAML 18 50 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 14 10 0 30 Mar 2022
Revisiting Neuron Coverage for DNN Testing: A Layer-Wise and Distribution-Aware Criterion Yuanyuan Yuan Qi Pang Shuai Wang 43 22 0 03 Dec 2021
A Review and Refinement of Surprise Adequacy Michael Weiss Rwiddhi Chakraborty Paolo Tonella AAML AI4TS 19 16 0 10 Mar 2021