Cluster analysis plays an indispensable role in machine learning and data mining. Learning
a good data representation is crucial for clustering algorithms. Recently, deep clustering …

In recent years, with the great success of deep learning and especially deep unsupervised
learning, many deep architectural clustering methods, collectively known as deep clustering …

Abstract Domain adaptive object re-ID aims to transfer the learned knowledge from the
labeled source domain to the unlabeled target domain to tackle the open-class re …

In this paper, a new deep incomplete multi-view clustering network, called DIMC-net, is
proposed to address the challenge of multi-view clustering on missing views. In particular …

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, deep multi-view clustering (MVC) has attracted increasing attention in multi-view
learning owing to its promising performance. However, most existing deep multi-view …

Deep clustering has increasingly been demonstrating superiority over conventional shallow
clustering algorithms. Deep clustering algorithms usually combine representation learning …

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 …

A clustering ensemble provides an elegant framework to learn a consensus result from
multiple prespecified clustering partitions. Though conventional clustering ensemble …

Learning representations for individual instances when only bag-level labels are available is
a fundamental challenge in multiple instance learning (MIL). Recent works have shown …