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

Isolation forest (iForest) has been emerging as arguably the most popular anomaly detector
in recent years due to its general effectiveness across different benchmarks and strong …

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 …

Detecting which nodes in graphs are outliers is a relatively new machine learning task with
numerous applications. Despite the proliferation of algorithms developed in recent years for …

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 …

The density-based method is a more widely used anomaly detection. However, most of the
existing density-based methods mainly focus on dealing with certainty data and do not …

Unsupervised anomaly detection is an important research direction in the process of
unsupervised knowledge acquisition. It has been successfully applied in many fields, such …

Deep learning (DL) techniques have recently found success in anomaly detection (AD)
across various fields such as finance, medical services, and cloud computing. However …

PyGOD is an open-source Python library for detecting outliers in graph data. As the first
comprehensive library of its kind, PyGOD supports a wide array of leading graph-based …