Uncertainty quantification (UQ) is an essential tool for applying deep neural networks (DNNs) to real world tasks, as it attaches a degree of confidence to DNN outputs. However, despite its benefits, UQ is often left out of the standard DNN workflow due to the additional technical knowledge required to apply and evaluate existing UQ procedures. Hence there is a need for a comprehensive toolbox that allows the user to integrate UQ into their modelling workflow, without significant overhead. We introduce \texttt{Lightning UQ Box}: a unified interface for applying and evaluating various approaches to UQ. In this paper, we provide a theoretical and quantitative comparison of the wide range of state-of-the-art UQ methods implemented in our toolbox. We focus on two challenging vision tasks: (i) estimating tropical cyclone wind speeds from infrared satellite imagery and (ii) estimating the power output of solar panels from RGB images of the sky. By highlighting the differences between methods our results demonstrate the need for a broad and approachable experimental framework for UQ, that can be used for benchmarking UQ methods. The toolbox, example implementations, and further information are available at: https://github.com/lightning-uq-box/lightning-uq-box

本研究解决了在深度神经网络 (DNN) 中未充分利用不确定性量化 (UQ) 的问题，提供了一个全面的工具箱，使用户能够轻松地将UQ集成到其建模流程中。我们提出的“Lightning UQ Box”统一接口允许对多种UQ方法进行应用和评估，实验证明其在风速估计和太阳能发电量预测等复杂任务中的有效性，具有广泛的适用潜力。

Lightning UQ Box：深度学习中不确定性量化的综合框架