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 …

Anomaly detection, aka outlier detection or novelty detection, has been a lasting yet active
research area in various research communities for several decades. There are still some …

Abstract Domain adaptation (DA) aims to transfer the knowledge learned from source
domain to an unlabeled target domain. Some recent works tackle source-free domain …

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 …

Clustering is a fundamental machine learning task, which aim at assigning instances into
groups so that similar samples belong to the same cluster while dissimilar samples belong …

Abstract Machine learning (ML) pervades an increasing number of academic disciplines and
industries. Its impact is profound, and several fields have been fundamentally altered by it …

Because of the ambiguous definition of anomaly and the complexity of real data, video
anomaly detection is one of the most challenging problems in intelligent video surveillance …

Unsupervised domain adaptation (UDA) is to make predictions for unlabeled data on a
target domain, given labeled data on a source domain whose distribution shifts from the …