  Assessing reliably the confidence of a deep neural network and predicting its
failures is of primary importance for the practical deployment of these models.
In this paper, we propose a new target criterion for model confidence,
corresponding to the True Class Probability (TCP). We show how using the TCP is
more suited than relying on the classic Maximum Class Probability (MCP). We
provide in addition theoretical guarantees for TCP in the context of failure
prediction. Since the true class is by essence unknown at test time, we propose
to learn TCP criterion on the training set, introducing a specific learning
scheme adapted to this context. Extensive experiments are conducted for
validating the relevance of the proposed approach. We study various network
architectures, small and large scale datasets for image classification and
semantic segmentation. We show that our approach consistently outperforms
several strong methods, from MCP to Bayesian uncertainty, as well as recent
approaches specifically designed for failure prediction.
