  Reliably assessing model confidence in deep learning and predicting errors
likely to be made are key elements in providing safety for model deployment, in
particular for applications with dire consequences. In this paper, it is first
shown that uncertainty-aware deep Dirichlet neural networks provide an improved
separation between the confidence of correct and incorrect predictions in the
true class probability (TCP) metric. Second, as the true class is unknown at
test time, a new criterion is proposed for learning the true class probability
by matching prediction confidence scores while taking imbalance and TCP
constraints into account for correct predictions and failures. Experimental
results show our method improves upon the maximum class probability (MCP)
baseline and predicted TCP for standard networks on several image
classification tasks with various network architectures.
