A review of uncertainty quantification in deep learning: Techniques, applications and challenges

Highlights

• We provided an extensive review of uncertainty quantification methods in deep learning.

• We covered popular and efficient Bayesian approaches for uncertainty quantification.

• We listed notable ensemble techniques for quantifying uncertainty.

• We discussed various applications of uncertainty quantification methods.

• We summarized major open challenges and research gaps in uncertainty quantification.

Abstract

Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of uncertainties during both optimization and decision making processes. They have been applied to solve a variety of real-world problems in science and engineering. Bayesian approximation and ensemble learning techniques are two widely-used types of uncertainty quantification (UQ) methods. 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, investigates the application of these methods in reinforcement learning, and highlights fundamental research challenges and directions associated with UQ.