A Survey on Uncertainty Quantification Methods for Deep Learning

Understand

Deep neural networks (DNNs) have achieved tremendous success in computer vision, natural language processing, and scientific and engineering domains.

• However, DNNs can make unexpected, incorrect, yet overconfident predictions, leading to serious consequences in high-stakes applications such as autonomous driving, medical diagnosis, and disaster response.
• Uncertainty quantification (UQ) estimates the confidence of DNN predictions in addition to their accuracy.
• In recent years, many UQ methods have been developed for DNNs.