Abstract
Deep clustering has recently emerged as a promising technique for complex image clustering. Despite the significant progress, previous deep clustering works mostly tend to construct the final clustering by utilizing a single layer of representation, e.g., by performing $K$-means on the last fully-connected layer or by associating some clustering loss to a specific layer. However, few of them have considered the possibilities and potential benefits of jointly leveraging multi-layer representations for enhancing the deep clustering performance. In light of this, this paper presents a Deep Clustering via Ensembles (DeepCluE) approach, which bridges the gap between deep clustering and ensemble clustering by harnessing the power of multiple layers in deep neural networks. Particularly, we utilize a weight-sharing convolutional neural network as the backbone, which is trained with both the instance-level contrastive learning (via an instance projector) and the cluster-level contrastive learning (via a cluster projector) in an unsupervised manner. Thereafter, multiple layers of feature representations are extracted from the trained network, upon which a set of diversified base clusterings can be generated via a highly efficient clusterer. Then, the reliability of the clusters in multiple base clusterings is automatically estimated by exploiting an entropy-based criterion, based on which the multiple base clusterings are further formulated into a weighted-cluster bipartite graph. By partitioning this bipartite graph via transfer cut, the final image clustering result can therefore be obtained. Experimental results on six image datasets confirm the advantages of our DeepCluE approach over the state-of-the-art deep clustering approaches.
Abstract (translated)
URL
https://arxiv.org/abs/2206.00359
