  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. In this work, we propose
differentiable losses to improve calibration based on a soft (continuous)
version of the binning operation underlying popular calibration-error
estimators. When incorporated into training, these soft calibration losses
achieve state-of-the-art single-model ECE across multiple datasets with less
than 1% decrease in accuracy. For instance, we observe an 82% reduction in ECE
(70% relative to the post-hoc rescaled ECE) in exchange for a 0.7% relative
decrease in accuracy relative to the cross entropy baseline on CIFAR-100. When
incorporated post-training, the soft-binning-based calibration error objective
improves upon temperature scaling, a popular recalibration method. Overall,
experiments across losses and datasets demonstrate that using
calibration-sensitive procedures yield better uncertainty estimates under
dataset shift than the standard practice of using a cross entropy loss and
post-hoc recalibration methods.
