  Neural networks have proven successful at learning from complex data
distributions by acting as universal function approximators. However, they are
often overconfident in their predictions, which leads to inaccurate and
miscalibrated probabilistic predictions. The problem of overconfidence becomes
especially apparent in cases where the test-time data distribution differs from
that which was seen during training. We propose a solution to this problem by
seeking out regions of feature space where the model is unjustifiably
overconfident, and conditionally raising the entropy of those predictions
towards that of the prior distribution of the labels. Our method results in a
better calibrated network and is agnostic to the underlying model structure, so
it can be applied to any neural network which produces a probability density as
an output. We demonstrate the effectiveness of our method and validate its
performance on both classification and regression problems, applying it to
recent probabilistic neural network models.
