Uncertainty-Wizard: Fast and User-Friendly Neural Network Uncertainty Quantification
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Abstract (arXiv)
Uncertainty and confidence have been shown to be useful metrics in a wide variety of techniques proposed for deep learning testing, including test data selection and system supervision.We present uncertainty-wizard, a tool that allows to quantify such uncertainty and confidence in artificial neural networks. It is built on top of the industry-leading tf.keras deep learning API and it provides a near-transparent and easy to understand interface. At the same time, it includes major performance optimizations that we benchmarked on two different machines and different configurations.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Dec 2020.
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Citing papers per year: 2021: 2; 2022: 5; 2023: 1; 2024: 1.
9 papers.
- 2022Simple Techniques Work Surprisingly Well for Neural Network Test Prioritization and Active Learning (Replicability Study)cited by 27
- 2021Fail-Safe Execution of Deep Learning based Systems through Uncertainty Monitoringcited by 11
- 2024Predicting Safety Misbehaviours in Autonomous Driving Systems using Uncertainty Quantificationcited by 11
- 2021Prediction Surface Uncertainty Quantification in Object Detection Models for Autonomous Drivingcited by 10
- 2022Generating and Detecting True Ambiguity: A Forgotten Danger in DNN Supervision Testingcited by 8
- 2022A Survey on Uncertainty Toolkits for Deep Learningcited by 4
- 2022Uncertainty Quantification for Deep Neural Networks: An Empirical Comparison and Usage Guidelinescited by 3
- 2022CheapET-3: Cost-Efficient Use of Remote DNN Modelscited by 1
- 2023Adopting Two Supervisors for Efficient Use of Large-Scale Remote Deep Neural Networkscited by 0
- 2024Predicting Safety Misbehaviours in Autonomous Driving Systems using Uncertainty Quantification29 Apr 2024
- 2023Adopting Two Supervisors for Efficient Use of Large-Scale Remote Deep Neural Networks5 Apr 2023
- 2022Uncertainty Quantification for Deep Neural Networks: An Empirical Comparison and Usage Guidelines14 Dec 2022
- 2022CheapET-3: Cost-Efficient Use of Remote DNN Models24 Aug 2022
- 2022Generating and Detecting True Ambiguity: A Forgotten Danger in DNN Supervision Testing21 Jul 2022
- 2022A Survey on Uncertainty Toolkits for Deep Learning2 May 2022
- 2022Simple Techniques Work Surprisingly Well for Neural Network Test Prioritization and Active Learning (Replicability Study)2 May 2022
- 2021Prediction Surface Uncertainty Quantification in Object Detection Models for Autonomous Driving11 Jul 2021
- 2021Fail-Safe Execution of Deep Learning based Systems through Uncertainty Monitoring1 Feb 2021