A Survey on Uncertainty Quantification for Deep Learning: An Uncertainty Source Perspective

arXiv:2302.13425v3 Announce Type: replace
Abstract: Deep neural networks (DNNs) have achieved tremendous success in making accurate predictions for computer vision, natural language processing, as well as science and engineering domains. However, it is also well-recognized that DNNs sometimes make unexpected, incorrect, but overconfident predictions. This can cause serious consequences in high-stake applications, such as autonomous driving, medical diagnosis, and disaster response. Uncertainty quantification (UQ) aims to estimate the confidence of DNN predictions beyond prediction accuracy. In recent years, many UQ …

abstract arxiv computer computer vision cs.lg deep learning domains engineering however language language processing making natural natural language natural language processing networks neural networks perspective predictions processing quantification science stat.ml success survey type uncertainty vision