Prototype-Based Feature Representation Learning for Image Deep Clustering

Recently, image clustering methods based on self-supervised learning have achieved great progress, improving both compactness within cluster and separation between clusters. Among them, multi-stage clustering methods introduce pseudo-supervised information to the model through pre-training stage, which brings better performance. But the performance of contrastive learning based clustering methods may be degenerated by the class collision issue caused by false negative pairs. To this end, a novel Prototype representation Learning framework for image Deep Clustering (PLDC) is proposed, which considers the constraints of both cluster and feature training, and reduces the risk of class collision by our designed learning pattern. First, initial feature learning is performed using non-negative sample-pairs, and the higher quality of the features generated, the better performance achieved in subsequent training. Second, a clustering network is trained for generating reliable cluster assignments as pseudo-label information. Third, a prototype-based representation learning refines feature representations and clusters quality. The latter two are iteratively optimized to improve clustering performance. Experimental results on five benchmark datasets demonstrate that PLDC outperforms the state-of-the-art image clustering methods. New levels of clustering accuracies 97.1% and 80.9% are achieved on ImageNet-10 and ImageNet-dogs, respectively.