一个统一框架，让深度学习模型轻松评估预测不确定性。
Torch-Uncertainty: A Deep Learning Framework for Uncertainty Quantification
- 基于PyTorch和Lightning构建，支持多种不确定性量化方法
- 在分类、分割、回归任务中全面对比了多种UQ方法性能
- 适合需要可靠置信度估计的医疗、自动驾驶等高风险场景
深度神经网络在计算机视觉和自然语言处理等领域表现出色，但往往难以准确量化预测不确定性，限制了其在关键现实应用中的推广。不确定性量化（UQ）旨在通过提供方法来提升预测可信度。尽管已有众多技术提出，但仍缺乏一个统一工具来实现UQ方法的无缝训练、评估与集成。为此，我们提出了Torch-Uncertainty，一个基于PyTorch和Lightning的框架，用于简化深度学习模型在各类任务中集成不确定性量化技术的流程。本文阐述了该库的设计原则，并展示了在分类、分割和回归任务上对多种UQ方法的系统性实验评估。该框架已开源，地址为https://github.com/ENSTA-U2IS-AI/Torch-Uncertainty。
原文摘要 · Abstract (English)
Deep Neural Networks (DNNs) have demonstrated remarkable performance across various domains, including computer vision and natural language processing. However, they often struggle to accurately quantify the uncertainty of their predictions, limiting their broader adoption in critical real-world applications. Uncertainty Quantification (UQ) for Deep Learning seeks to address this challenge by providing methods to improve the reliability of uncertainty estimates. Although numerous techniques have been proposed, a unified tool offering a seamless workflow to evaluate and integrate these methods remains lacking. To bridge this gap, we introduce Torch-Uncertainty, a PyTorch and Lightning-based framework designed to streamline DNN training and evaluation with UQ techniques and metrics. In this paper, we outline the foundational principles of our library and present comprehensive experimental results that benchmark a diverse set of UQ methods across classification, segmentation, and regression tasks. Our library is available at https://github.com/ENSTA-U2IS-AI/Torch-Uncertainty
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