  Deep learning models frequently make incorrect predictions with high
confidence when presented with test examples that are not well represented in
their training dataset. We propose a novel and straightforward approach to
estimate prediction uncertainty in a pre-trained neural network model. Our
method estimates the training data density in representation space for a novel
input. A neural network model then uses this information to determine whether
we expect the pre-trained model to make a correct prediction. This uncertainty
model is trained by predicting in-distribution errors, but can detect
out-of-distribution data without having seen any such example. We test our
method for a state-of-the art image classification model in the settings of
both in-distribution uncertainty estimation as well as out-of-distribution
detection.
