  To increase the trustworthiness of deep neural network (DNN) classifiers, an
accurate prediction confidence that represents the true likelihood of
correctness is crucial. Towards this end, many post-hoc calibration methods
have been proposed to leverage a lightweight model to map the target DNN's
output layer into a calibrated confidence. Nonetheless, on an
out-of-distribution (OOD) dataset in practice, the target DNN can often
mis-classify samples with a high confidence, creating significant challenges
for the existing calibration methods to produce an accurate confidence. In this
paper, we propose a new post-hoc confidence calibration method, called CCAC
(Confidence Calibration with an Auxiliary Class), for DNN classifiers on OOD
datasets. The key novelty of CCAC is an auxiliary class in the calibration
model which separates mis-classified samples from correctly classified ones,
thus effectively mitigating the target DNN's being confidently wrong. We also
propose a simplified version of CCAC to reduce free parameters and facilitate
transfer to a new unseen dataset. Our experiments on different DNN models,
datasets and applications show that CCAC can consistently outperform the prior
post-hoc calibration methods.
