Misclassification detection is an important problem in machine learning, as it allows for the identification of instances where the model's predictions are unreliable. However, conventional uncertainty measures such as Shannon entropy do not provide an effective way to infer the real uncertainty associated with the model's predictions. In this paper, we introduce a novel data-driven measure of relative uncertainty to an observer for misclassification detection. By learning patterns in the distribution of soft-predictions, our uncertainty measure can identify misclassified samples based on the predicted class probabilities. Interestingly, according to the proposed measure, soft-predictions that correspond to misclassified instances can carry a large amount of uncertainty, even though they may have low Shannon entropy. We demonstrate empirical improvements over multiple image classification tasks, outperforming state-of-the-art misclassification detection methods.

引入了一种针对机器学习中误分检测的新型数据驱动相对不确定性度量方法，通过学习软预测的分布模式，可以根据预测的类别概率识别误分样本，并在多个图像分类任务中表现出超过现有误分检测方法的实证效果。

基于数据驱动的相对不确定度度量方法用于误分类检测