Uncertainty-Wizard: Fast and User-Friendly Neural Network Uncertainty Quantification
This tool addresses the problem of efficiently quantifying uncertainty in neural networks for deep learning practitioners, which is useful for tasks like test data selection and system supervision.
The paper introduces Uncertainty-Wizard, a tool built on tf.keras for quantifying uncertainty and confidence in neural networks. It offers a user-friendly interface and significant performance optimizations, which were benchmarked across various configurations and machines.
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.