论文标题
深度学习中不确定性量化的综述：技术，应用和挑战
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
论文作者
论文摘要
不确定性定量（UQ）在优化和决策过程中的不确定性减少中起关键作用。它可以应用于解决科学和工程中的各种现实应用程序。贝叶斯近似和集合学习技术是文献中两种最广泛使用的UQ方法。 In this regard, researchers have proposed different UQ methods and examined their performance in a variety of applications such as computer vision (e.g., self-driving cars and object detection), image processing (e.g., image restoration), medical image analysis (e.g., medical image classification and segmentation), natural language processing (e.g., text classification, social media texts and recidivism risk-scoring), bioinformatics, etc. This study reviews recent深度学习中使用的UQ方法的进步。此外，我们还研究了这些方法在加固学习（RL）中的应用。然后，我们概述了UQ方法的一些重要应用。最后，我们简要强调了UQ方法面临的基本研究挑战，并讨论了该领域的未来研究方向。
Uncertainty quantification (UQ) plays a pivotal role in reduction of uncertainties during both optimization and decision making processes. It can be applied to solve a variety of real-world applications in science and engineering. Bayesian approximation and ensemble learning techniques are two most widely-used UQ methods in the literature. In this regard, researchers have proposed different UQ methods and examined their performance in a variety of applications such as computer vision (e.g., self-driving cars and object detection), image processing (e.g., image restoration), medical image analysis (e.g., medical image classification and segmentation), natural language processing (e.g., text classification, social media texts and recidivism risk-scoring), bioinformatics, etc. This study reviews recent advances in UQ methods used in deep learning. Moreover, we also investigate the application of these methods in reinforcement learning (RL). Then, we outline a few important applications of UQ methods. Finally, we briefly highlight the fundamental research challenges faced by UQ methods and discuss the future research directions in this field.
