As industries become automated and connectivity technologies advance, a wide range of
systems continues to generate massive amounts of data. Many approaches have been …

Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …

Given a long list of anomaly detection algorithms developed in the last few decades, how do
they perform with regard to (i) varying levels of supervision,(ii) different types of anomalies …

Abstract Out-of-distribution (OOD) detection is a critical task for deploying machine learning
models in the open world. Distance-based methods have demonstrated promise, where …

Detecting anomalous subsequences in time series data is an important task in areas
ranging from manufacturing processes over finance applications to health care monitoring …

Given high-dimensional time series data (eg, sensor data), how can we detect anomalous
events, such as system faults and attacks? More challengingly, how can we do this in a way …

Abstract 2D-based Industrial Anomaly Detection has been widely discussed, however,
multimodal industrial anomaly detection based on 3D point clouds and RGB images still has …

Multimodal time series (MTS) anomaly detection is crucial for maintaining the safety and
stability of working devices (eg, water treatment system and spacecraft), whose data are …

PyOD is an open-source Python toolbox for performing scalable outlier detection on
multivariate data. Uniquely, it provides access to a wide range of outlier detection …

Traditional distance and density-based anomaly detection techniques are unable to detect
periodic and seasonality related point anomalies which occur commonly in streaming data …