Active Deep Image Clustering

Deep clustering has attracted increasingly more attention in recent years. However, due to the absence of labels, deep clustering sometimes still provides unreliable clustering results. Although semi-supervised deep clustering can alleviate this problem to some extent by involving few human annotations, we observe that the performance of semi-supervised clustering highly depends on the selection of data for human labeling, but unfortunately, the supervised information selection is still a tough problem as traditional semi-supervised methods pay no attention to it. To tackle this problem, in this paper, we propose a novel deep active clustering method, which can actively select the key data for human labeling and apply the human annotations to improve the deep clustering. Different from conventional semi-supervised deep clustering methods which use fixed pre-given supervised information, we design a simple yet effective strategy to select the informative and uncertain data for querying annotation, which is beneficial to the clustering task. Furthermore, we integrate deep representation learning, clustering, and data selection into a unified framework, so that each task can be boosted by each other. Finally, we conduct extensive experiments on benchmark data sets by comparing it with some state-of-the-art deep clustering methods and semi-supervised clustering methods. The experimental results show that our active clustering methods can outperform both the unsupervised and semi-supervised clustering methods, demonstrating the effectiveness of the proposed method. The codes of this paper are released in https://doctor-nobody.github.io/codes/ADC_codes.zip.