As an important technique for data pre-processing, outlier detection plays a crucial role in
various real applications and has gained substantial attention, especially in medical fields …

Time series anomaly detection is a challenging problem due to the complex temporal
dependencies and the limited label data. Although some algorithms including both …

Manipulated videos often contain subtle inconsistencies between their visual and audio
signals. We propose a video forensics method, based on anomaly detection, that can …

Deep probabilistic generative models enable modeling the likelihoods of very high
dimensional data. An important application of generative modeling should be the ability to …

Time series anomaly detection is critical for a wide range of applications. It aims to identify
deviant samples from the normal sample distribution in time series. The most fundamental …

Vehicular Ad hoc Network (VANET) is an enabling technology to provide a variety of
convenient services in intelligent transportation systems, and yet vulnerable to various …

Scarcity of annotated images hampers the building of automated solution for reliable COVID-
19 diagnosis and evaluation from CT. To alleviate the burden of data annotation, we herein …

Weakly supervised anomaly detection aims at learning an anomaly detector from a limited
amount of labeled data and abundant unlabeled data. Recent works build deep neural …

Many well-established anomaly detection methods use the distance of a sample to those in
its local neighbourhood: so-calledlocal outlier methods', such as LOF and DBSCAN. They …

Unsupervised anomaly segmentation methods based on knowledge distillation have
recently been developed and have shown superior segmentation performance. However …