This paper proposes to perform online clustering by conducting twin contrastive learning
(TCL) at the instance and cluster level. Specifically, we find that when the data is projected …

In this paper, we propose an online clustering method called Contrastive Clustering (CC)
which explicitly performs the instance-and cluster-level contrastive learning. To be specific …

As an indispensable branch of unsupervised learning, deep clustering is rapidly emerging
along with the growth of deep neural networks. Recently, contrastive learning paradigm has …

Contrastive learning is widely used in deep image clustering due to its ability to learn
discriminative representations. However, some studies simply combined contrastive …

In this paper, a novel D eep M ulti-view J oint C lustering (DMJC) framework is proposed,
where multiple deep embedded features, multi-view fusion mechanism, and clustering …

Datasets are often collected from different resources or comprised of multiple
representations (ie, views). Multi-view clustering aims to analyze the multi-view data in an …

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 …

Existing deep clustering methods rely on either contrastive or non-contrastive representation
learning for downstream clustering task. Contrastive-based methods thanks to negative …

Deep multi-view clustering (MVC) is to mine and employ the complex relationships among
views to learn the compact data clusters with deep neural networks in an unsupervised …

The similarity among samples and the discrepancy among clusters are two crucial aspects
of image clustering. However, current deep clustering methods suffer from inaccurate …