A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
It provides a comprehensive overview for researchers and practitioners in ML/AI, but is incremental as it synthesizes existing work without new results.
This paper reviews recent advances in uncertainty quantification (UQ) methods in deep learning, covering techniques like Bayesian approximation and ensemble learning, and investigates their applications in areas such as computer vision, medical image analysis, and reinforcement learning, while also outlining challenges and future directions.
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.