Data clustering is a commonly used data processing technique in many fields, which divides
objects into different clusters in terms of some similarity measure between data points …

In this paper, we propose a recurrent framework for joint unsupervised learning of deep
representations and image clusters. In our framework, successive operations in a clustering …

In this paper, we propose a new clustering model, called DEeP Embedded RegularIzed
ClusTering (DEPICT), which efficiently maps data into a discriminative embedding subspace …

Abstract Generative Adversarial networks (GANs) have obtained remarkable success in
many unsupervised learning tasks and unarguably, clustering is an important unsupervised …

Density peaks clustering (DPC) algorithm is a new algorithm based on density clustering
analysis, which can quickly obtain the cluster centers by drawing the decision diagram by …

In this paper, we propose a novel approach of learning mid-level filters from automatically
discovered patch clusters for person re-identification. It is well motivated by our study on …

Hierarchical semantic structures naturally exist in an image dataset, in which several
semantically relevant image clusters can be further integrated into a larger cluster with …

Clustering is a fundamental procedure in the analysis of scientific data. It is used
ubiquitously across the sciences. Despite decades of research, existing clustering …

Finetuning from a pretrained deep model is found to yield state-of-the-art performance for
many vision tasks. This paper investigates many factors that influence the performance in …

Groups are the primary entities that make up a crowd. Understanding group-level dynamics
and properties is thus scientifically important and practically useful in a wide range of …