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 successfully provides more effective features than conventional ones and
thus becomes an important technique in current unsupervised learning. However, most …

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

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

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 …

Deep embedded clustering is a popular unsupervised learning method owing to its
outstanding performance in data-mining applications. However, existing methods ignore the …

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

Deep clustering can optimize representations of instances (ie, representation learning) and
explore the inherent data distribution (ie, clustering) simultaneously, which demonstrates a …

Deep clustering has shown its promising capability in joint representation learning and
clustering via deep neural networks. Despite the significant progress, the existing deep …