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 …

We present Mixture of Contrastive Experts (MiCE), a unified probabilistic clustering
framework that simultaneously exploits the discriminative representations learned by …

Recent advances in self-supervised learning with instance-level contrastive objectives
facilitate unsupervised clustering. However, a standalone datum is not perceiving the …

Clustering is an essential task for the purpose that data points can be classified in an
unsupervised manner. Most deep clustering algorithms are very effective when given the …

Contrastive-based clustering models usually rely on a large number of negative pairs to
capture uniform representations, which requires a large batch size and high computational …

Clustering is a fundamental challenge in many data-driven application fields and machine
learning techniques. The data distribution determines the quality of the outcomes, which has …

Self-supervised clustering methods have achieved increasing accuracy in recent years but
do not yet perform as well as supervised classification methods. This contrasts with the …

This passage introduces a new clustering approach called BYOL network-based Contrastive
Clustering (BCC). This methodology builds on the BYOL framework, which consists of two co …

We propose the new task of K principal concept identification for dataset summarizarion. The
objective is to find a set of K concepts that best explain the variation within the dataset …

Image clustering methods have rapidly improved their ability to discover object categories.
However, unsupervised clustering methods struggle on other image attributes, eg age or …