  Reliably quantifying the confidence of deep neural classifiers is a
challenging yet fundamental requirement for deploying such models in
safety-critical applications. In this paper, we introduce a novel target
criterion for model confidence, namely the true class probability (TCP). We
show that TCP offers better properties for confidence estimation than standard
maximum class probability (MCP). Since the true class is by essence unknown at
test time, we propose to learn TCP criterion from data with an auxiliary model,
introducing a specific learning scheme adapted to this context. We evaluate our
approach on the task of failure prediction and of self-training with
pseudo-labels for domain adaptation, which both necessitate effective
confidence estimates. Extensive experiments are conducted for validating the
relevance of the proposed approach in each task. We study various network
architectures and experiment with small and large datasets for image
classification and semantic segmentation. In every tested benchmark, our
approach outperforms strong baselines.
