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.

本研究介绍了基于不确定性的深度狄利克雷神经网络可靠地评估模型置信度和预测错误，提出新的准则基于置信度分数匹配来学习正确预测和失败的真实类别概率，并在多个图像分类任务上取得了优于传统网络最大类概率的预测真实类别概率的实验结果。