  When the cost of misclassifying a sample is high, it is useful to have an
accurate estimate of uncertainty in the prediction for that sample. There are
also multiple types of uncertainty which are best estimated in different ways,
for example, uncertainty that is intrinsic to the training set may be
well-handled by a Bayesian approach, while uncertainty introduced by shifts
between training and query distributions may be better-addressed by
density/support estimation. In this paper, we examine three types of
uncertainty: model capacity uncertainty, intrinsic data uncertainty, and open
set uncertainty, and review techniques that have been derived to address each
one. We then introduce a unified hierarchical model, which combines methods
from Bayesian inference, invertible latent density inference, and
discriminative classification in a single end-to-end deep neural network
topology to yield efficient per-sample uncertainty estimation in a detection
context. This approach addresses all three uncertainty types and can readily
accommodate prior/base rates for binary detection. We then discuss how to
extend this model to a more generic multiclass recognition context.
