Uncertainty-Wizard: Fast and User-Friendly Neural Network Uncertainty Quantification
2020-12-29Code Available1· sign in to hype
Michael Weiss, Paolo Tonella
Code Available — Be the first to reproduce this paper.
ReproduceCode
- github.com/testingautomated-usi/uncertainty-wizardOfficialIn papertf★ 44
Abstract
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.