一站式不确定性量化工具箱，让深度学习模型输出更可信。
Lightning UQ Box: A Comprehensive Framework for Uncertainty Quantification in Deep Learning
- 统一接口集成多种不确定性量化方法，降低使用门槛。
- 在台风风速和太阳能发电量预测任务中验证效果，提升可靠性。
- 适合需要可信输出的科研与工业应用，如气象、能源领域。
不确定性量化（UQ）是将深度神经网络应用于现实任务的关键工具，可为模型输出提供置信度。然而，由于实施和评估现有UQ方法需额外技术知识，其常被排除在标准深度学习工作流之外。为此，我们提出\texttt{Lightning UQ Box}：一个统一接口，用于应用和评估多种前沿的不确定性量化方法。本文对工具箱中实现的各类先进UQ方法进行了理论与定量比较，聚焦两个具有挑战性的视觉任务：(i) 从红外卫星图像估计热带气旋风速，(ii) 从天空RGB图像估计太阳能板功率输出。通过揭示不同方法的差异，结果表明亟需一个全面且易用的实验框架，以支持UQ方法的基准测试。工具箱、示例实现及更多信息详见：https://github.com/lightning-uq-box/lightning-uq-box。
原文摘要 · Abstract (English)
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
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