标题： 使用温度缩放的Dropout变分推断的校准模型不确定性 显示英文标题

标题： Well-calibrated Model Uncertainty with Temperature Scaling for Dropout Variational Inference

摘要： 通过变分贝叶斯推断与蒙特卡洛丢弃获得的模型不确定性容易出现校准错误。 该不确定性不能很好地表示模型误差。 在本文中，温度缩放被扩展到丢弃变分推断以校准模型不确定性。 提出了期望不确定性校准误差（UCE）作为衡量不确定性校准错误的指标。 该方法的有效性在最近的CNN架构上的CIFAR-10/100数据集上进行了评估。 实验结果表明，温度缩放通过UCE显著减少了校准错误，并实现了对不确定预测的稳健拒绝。 所提出的方法可以很容易地从频率论温度缩放中推导出来，并产生校准良好的模型不确定性。 它易于实现且不影响模型准确性。 显示英文摘要

摘要： Model uncertainty obtained by variational Bayesian inference with Monte Carlo dropout is prone to miscalibration. The uncertainty does not represent the model error well. In this paper, temperature scaling is extended to dropout variational inference to calibrate model uncertainty. Expected uncertainty calibration error (UCE) is presented as a metric to measure miscalibration of uncertainty. The effectiveness of this approach is evaluated on CIFAR-10/100 for recent CNN architectures. Experimental results show, that temperature scaling considerably reduces miscalibration by means of UCE and enables robust rejection of uncertain predictions. The proposed approach can easily be derived from frequentist temperature scaling and yields well-calibrated model uncertainty. It is simple to implement and does not affect the model accuracy.