Image clustering is a crucial but challenging task in machine learning and computer vision.
Existing methods often ignore the combination between feature learning and clustering. To …

We propose a self-supervised Gaussian ATtention network for image Clustering
(GATCluster). Rather than extracting intermediate features first and then performing …

Recent developed deep unsupervised methods allow us to jointly learn representation and
cluster unlabelled data. These deep clustering methods% like DAC start with mainly focus …

Traditional image clustering methods take a two-step approach, feature learning and
clustering, sequentially. However, recent research results demonstrated that combining the …

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

Clustering has been a major research topic in the field of machine learning, one to which
Deep Learning has recently been applied with significant success. However, an aspect of …

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 …

Deep clustering which adopts deep neural networks to obtain optimal representations for
clustering has been widely studied recently. In this paper, we propose a novel deep image …

Deep clustering gains superior performance than conventional clustering by jointly
performing feature learning and cluster assignment. Although numerous deep clustering …

Deep clustering gradually becomes an important branch in unsupervised learning methods.
However, current approaches hardly take into consideration the semantic sample …