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Reliably assessing model confidence in deep learning and predicting errorslikely to be made are key elements in providing safety for model deployment, inparticular for applications with dire consequences. In this paper, it is firstshown that uncertainty-aware deep Dirichlet neural networks provide an improvedseparation between the confidence of correct and incorrect predictions in thetrue class probability (TCP) metric. Second, as the true class is unknown attest time, a new criterion is proposed for learning the true class probabilityby matching prediction confidence scores while taking imbalance and TCPconstraints into account for correct predictions and failures. Experimentalresults show our method improves upon the maximum class probability (MCP)baseline and predicted TCP for standard networks on several imageclassification tasks with various network architectures.

Failure Prediction by Confidence Estimation of Uncertainty-Aware Dirichlet Networks