  Deep Neural Networks (DNNs) have achieved state-of-the-art accuracy
performance in many tasks. However, recent works have pointed out that the
outputs provided by these models are not well-calibrated, seriously limiting
their use in critical decision scenarios. In this work, we propose to use a
decoupled Bayesian stage, implemented with a Bayesian Neural Network (BNN), to
map the uncalibrated probabilities provided by a DNN to calibrated ones,
consistently improving calibration. Our results evidence that incorporating
uncertainty provides more reliable probabilistic models, a critical condition
for achieving good calibration. We report a generous collection of experimental
results using high-accuracy DNNs in standardized image classification
benchmarks, showing the good performance, flexibility and robust behavior of
our approach with respect to several state-of-the-art calibration methods. Code
for reproducibility is provided.
