Abstract: Image clustering is an important and open problem in image processing.Recently,some methods combine the powerful representation ability of contrastive learning to carry out end-to-end clustering learning and utilize the pseudo-label technique to improve the robustness of clustering methods.In the existing pseudo-label methods,we need to set a large threshold parameter to obtain highly confident samples to generate one-hot pseudo-labels and often cannot obtain enough highly confident samples.To make up for these defects,we propose a stratified pseudo-label clustering(SPC) method,which aims to train and refine the classification model using both structure and pseudo-labels information.We first introduce three assumptions for designing of deep clustering methods,i.e.,local smoothing assumption,self-training assumption,and low-density separation assumption.The me-thod consists of two stages:1)manifold based consistency learning,which is used to initialize the classification model in the trai-ning stage;and 2)stratified pseudo-label based model tefinement,which generates stratified pseudo-labels to improve the robustness of the clustering model.We first generate a strong pseudo-label dataset and a weak pseudo-label dataset with a threshold parameter,and then propose a label-propagation method and a mix-up method to improve the weak pseudo-label dataset.Finally,we use both strong pseudo-label dataset and weak pseudo-label dataset to refine the clustering model.Compared with the best baseline,the averaged ACC of SPC improves by 7.6% and 5.0% on STL10 and CIFAR100-20 benchmark datasets,respectively.

Key words: Deep clustering, Consistency learning, Pseudo-labels, Label propagation, Self-training learning