Improved deep clustering model based on semantic consistency for image clustering

Abstract

Recently, contrastive learning has gained increasing attention as a research topic for image-clustering tasks. However, most contrastive learning-based clustering models focus only on the similarity of embedded features or divergence of cluster assignments, without considering the semantic distribution of instances, undermining the performance of clustering. Therefore, an improved deep clustering model based on semantic consistency (DCSC) was proposed in this study, motivated by the assumption that the semantic probability distribution of various augmentations of the same instance should be similar and that of different instances should be orthogonal. The DCSC fully exploits instance-level differentiation, cluster-level discrimination, and semantic consistency of instances to design the objective function. Compared with existing contrastive learning-based clustering models, the proposed model is more cluster-sensitive to differentiate semantic concepts owing to the incorporation of cluster structure discovering loss. Extensive experimental results on six benchmark datasets illustrate that the proposed DCSC achieves superior performance compared to the state-of-the-art clustering models, with an improved accuracy of 9.3% for CIFAR-100 and 22.1% for tiny-ImageNet. The visualization results show that the DCSC produces geometrically well-separated cluster embeddings defined by the Euclidean distance, verifying the effectiveness of the proposed DCSC.