A review of uncertainty quantification in deep learning : techniques, applications and challenges
Abdar, Moloud; Pourpanah, Farhad; Hussain, Sadiq; Rezazadegan, Dana; Liu, Li; Ghavamzadeh, Mohammad; Fieguth, Paul; Cao, Xiaochun; Khosravi, Abbas; Acharya, U. Rajendra; Makarenkov, Vladimir; Nahavandi, Saeid (2021-05-23)
Moloud Abdar, Farhad Pourpanah, Sadiq Hussain, Dana Rezazadegan, Li Liu, Mohammad Ghavamzadeh, Paul Fieguth, Xiaochun Cao, Abbas Khosravi, U. Rajendra Acharya, Vladimir Makarenkov, Saeid Nahavandi, A review of uncertainty quantification in deep learning: Techniques, applications and challenges, Information Fusion, Volume 76, 2021, Pages 243-297, ISSN 1566-2535, https://doi.org/10.1016/j.inffus.2021.05.008
© 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
https://creativecommons.org/licenses/by-nc-nd/4.0/
https://urn.fi/URN:NBN:fi-fe2021090645179
Tiivistelmä
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.
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