Deep learning for anomaly detection: A review
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 …
research area in various research communities for several decades. There are still some …
Machine learning for reliability engineering and safety applications: Review of current status and future opportunities
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 …
industries. Its impact is profound, and several fields have been fundamentally altered by it …
Generalized source-free domain adaptation
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 …
domain to an unlabeled target domain. Some recent works tackle source-free domain …
Contrastive clustering
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 …
which explicitly performs the instance-and cluster-level contrastive learning. To be specific …
Video anomaly detection with spatio-temporal dissociation
Anomaly detection in videos remains a challenging task due to the ambiguous definition of
anomaly and the complexity of visual scenes from real video data. Different from the …
anomaly and the complexity of visual scenes from real video data. Different from the …
Deep clustering: A comprehensive survey
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 …
a good data representation is crucial for clustering algorithms. Recently, deep clustering …
Clustering driven deep autoencoder for video anomaly detection
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 …
anomaly detection is one of the most challenging problems in intelligent video surveillance …
Attributed graph clustering: A deep attentional embedding approach
Graph clustering is a fundamental task which discovers communities or groups in networks.
Recent studies have mostly focused on developing deep learning approaches to learn a …
Recent studies have mostly focused on developing deep learning approaches to learn a …
Unsupervised domain adaptation via structurally regularized deep clustering
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 …
target domain, given labeled data on a source domain whose distribution shifts from the …
A survey of clustering with deep learning: From the perspective of network architecture
Clustering is a fundamental problem in many data-driven application domains, and
clustering performance highly depends on the quality of data representation. Hence, linear …
clustering performance highly depends on the quality of data representation. Hence, linear …