  Evaluating a neural network on an input that differs markedly from the
training data might cause erratic and flawed predictions. We study a method
that judges the unusualness of an input by evaluating its informative content
compared to the learned parameters. This technique can be used to judge whether
a network is suitable for processing a certain input and to raise a red flag
that unexpected behavior might lie ahead. We compare our approach to various
methods for uncertainty evaluation from the literature for various datasets and
scenarios. Specifically, we introduce a simple, effective method that allows to
directly compare the output of such metrics for single input points even if
these metrics live on different scales.
