  In this paper we introduce a novel way of estimating prediction uncertainty
in deep networks through the use of uncertainty surrogates. These surrogates
are features of the penultimate layer of a deep network that are forced to
match predefined patterns. The patterns themselves can be, among other
possibilities, a known visual symbol. We show how our approach can be used for
estimating uncertainty in prediction and out-of-distribution detection.
Additionally, the surrogates allow for interpretability of the ability of the
deep network to learn and at the same time lend robustness against adversarial
attacks. Despite its simplicity, our approach is superior to the
state-of-the-art approaches on standard metrics as well as computational
efficiency and ease of implementation. A wide range of experiments are
performed on standard datasets to prove the efficacy of our approach.
