  The model uncertainty obtained by variational Bayesian inference with Monte
Carlo dropout is prone to miscalibration. In this paper, different logit
scaling methods are extended to dropout variational inference to recalibrate
model uncertainty. Expected uncertainty calibration error (UCE) is presented as
a metric to measure miscalibration. The effectiveness of recalibration is
evaluated on CIFAR-10/100 and SVHN for recent CNN architectures. Experimental
results show that logit scaling considerably reduce miscalibration by means of
UCE. Well-calibrated uncertainty enables reliable rejection of uncertain
predictions and robust detection of out-of-distribution data.
