A Weakly Supervised Learning Based Clustering Framework

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.