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.

本研究回顾了当前深度学习中不确定性量化（UQ）方法的最新进展，其应用于计算机视觉，医学图像分析，自然语言处理等领域，并调查了这些方法在强化学习中的应用。接着，我们概述了 UQ 方法的几个重要应用，并简要概述了 UQ 方法面临的基本研究挑战，讨论了未来在此领域的研究方向。

深度学习中的不确定性量化综述：技术、应用与挑战

A Review of Uncertainty Quantification in Deep Learning: Techniques,  Applications and Challenges

We propose a Bayesian approximation to a deep learning architecture for 3D
hand pose estimation. Through this framework, we explore and analyse the two
types of uncertainties that are influenced either by data or by the learning
capability. Furthermore, we draw comparisons against the standard estimator
over three popular benchmarks. The first contribution lies in outperforming the
baseline while in the second part we address the active learning application.
We also show that with a newly proposed acquisition function, our Bayesian 3D
hand pose estimator obtains lowest errors with the least amount of data. The
underlying code is publicly available at
this https URL

本文提出了一种基于贝叶斯近似的深度学习框架用于 3D 手势估计，通过该框架提取数据或者学习能力两种不确定性，与基准估计方法相比得到了更好的效果，并应用于主动学习应用中，采用新式获取函数，使用最小量数据得到了最低误差。