Abstract: As a basic unsupervised learning task,clustering aims to divide unlabeled and mixed images into semantically similar classes.Some recent approaches focus on the ability of the model to discriminate between different semantic classes by introducing data augmentation,using contrastive learning methods to learn feature representations and cluster assignments,which may lead to situations that feature embeddings from samples with the same semantic class are separated.Aiming at the above problems,a comparative clustering method with consistent structural relations(CCR) is proposed,which performs comparative learning at the instance level and cluster level,and adds consistency constraints at the relationship level.So that the model can learn more information of ‘positive data pair' and reduce the impact of cluster embedding being separated.Experimental results show that CCR obtains better results than the unsupervised clustering methods in recent years on the image benchmark dataset.The average accuracy on the CIFAR-10 and STL-10 datasets improves by 1.7% compared to the best methods in the same experimental settings and improves by 1.9% on the CIFAR-100 dataset.

Key words: Unsupervised learning, Clustering, Contrastive learning, Data Augmentation, Over clustering