The examination of uncertainty in the predictions of machine learning (ML) models is receiving increasing attention. One uncertainty modeling technique used for this purpose is Monte-Carlo (MC)-Dropout, where repeated predictions are generated for a single input. Therefore, clustering is required to describe the resulting uncertainty, but only through efficient clustering is it possible to describe the uncertainty from the model attached to each object. This article uses Bayesian Gaussian Mixture (BGM) to solve this problem. In addition, we investigate different values for the dropout rate and other techniques, such as focal loss and calibration, which we integrate into the Mask-RCNN model to obtain the most accurate uncertainty approximation of each instance and showcase it graphically.

翻译：机器学习模型预测中的不确定性研究正日益受到关注。蒙特卡洛丢弃法（MC-Dropout）是一种用于此目的的不确定性建模技术，它对单个输入生成重复预测。因此，需要聚类来描述由此产生的不确定性，但只有通过高效的聚类方法，才能描述模型赋予每个对象的不确定性。本文采用贝叶斯高斯混合模型（Bayesian Gaussian Mixture, BGM）来解决这一问题。此外，我们研究了丢弃率的不同取值及其他技术（如焦点损失和校准），并将其集成到Mask-RCNN模型中，以获取每个实例最准确的不确定性近似，并以图形方式展示。