A weakly supervised learning based clustering framework is proposed in this paper. As the core of this framework, we introduce a novel multiple instance learning task based on a bag level label called unique class count ($ucc$), which is the number of unique classes among all instances inside the bag. In this task, no annotations on individual instances inside the bag are needed during training of the models. We mathematically prove that a perfect $ucc$ classifier, in principle, can be used to perfectly cluster individual instances inside the bags. In other words, perfect clustering of individual instances is possible even when no annotations on individual instances are given during training. We have constructed a neural network based $ucc$ classifier and experimentally shown that the clustering performance of our framework with our $ucc$ classifier is comparable to that of fully supervised learning models. We have also observed that our $ucc$ classifiers can potentially be used for zero-shot learning as they learn better semantic features than fully supervised models for `unseen classes', which have never been input into the models during training.

本文提出了一种基于弱监督学习的聚类框架，其中介绍了一种基于多示例学习任务的新颖技术，称为唯一类计数（$ ucc $），该任务中不需要在训练过程中对单个实例进行注释。我们构建了一个基于神经网络的$ ucc $分类器，并在实验中证明，弱监督的$ ucc $分类器的聚类性能与完全监督学习模型的聚类性能相当。此外，我们测试了该框架在乳腺癌转移的语义分割任务中的适用性，并显示了弱监督的框架的性能与完全监督的 Unet 模型的性能相当。

利用独特类别计数进行弱监督聚类