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 …

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 …

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 …

Deep clustering has attracted increasing attention in recent years due to its capability of joint
representation learning and clustering via deep neural networks. In its latest developments …

Recently, some contrastive learning methods have been proposed to simultaneously learn
representations and clustering assignments, achieving significant improvements. However …

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

In this paper, we address Novel Class Discovery (NCD), the task of unveiling new classes in
a set of unlabeled samples given a labeled dataset with known classes. We exploit the …

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 …

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 …

Recently, many unsupervised deep learning methods have been proposed to learn
clustering with unlabelled data. By introducing data augmentation, most of the latest …