  We introduce a new, reliable, and agnostic uncertainty measure for
classification tasks called logit uncertainty. It is based on logit outputs of
neural networks. We in particular show that this new uncertainty measure yields
a superior performance compared to existing uncertainty measures on different
tasks, including out of sample detection and finding erroneous predictions. We
analyze theoretical foundations of the measure and explore a relationship with
high density regions. We also demonstrate how to test uncertainty using
intermediate outputs in training of generative adversarial networks. We propose
two potential ways to utilize logit-based uncertainty in real world
applications, and show that the uncertainty measure outperforms.
