Soft Calibration Objectives for Neural Networks

Optimal decision making requires that classifiers produce uncertainty estimates consistent with their empirical accuracy. However, deep neural networks are often under-or over-confident in their predictions. Consequently, methods have been developed to improve the calibration of their predictive uncertainty, both during training and post-hoc.