Torch-Uncertainty: A Deep Learning Framework for Uncertainty Quantification

📝 Original Info

• Title: Torch-Uncertainty: A Deep Learning Framework for Uncertainty Quantification
• ArXiv ID: 2511.10282
• Date: 2025-11-13
• Authors: ** 논문에 명시된 저자 정보가 제공되지 않았습니다. (저자명 및 소속을 확인하려면 원문 PDF 또는 arXiv 페이지를 참고하시기 바랍니다.) **

📝 Abstract

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

💡 Deep Analysis

📄 Full Content

📸 Image Gallery

Reference