Anomaly detection has been used for decades to identify and extract anomalous
components from data. Many techniques have been used to detect anomalies. One of the …

Anomaly detection has been used to detect and analyze anomalous elements from data for
years. Various techniques have been developed to detect anomalies. However, the most …

Anomaly detection is a widely explored domain in machine learning. Many models are
proposed in the literature, and compared through different metrics measured on various …

Although deep learning has been applied to successfully address many data mining
problems, relatively limited work has been done on deep learning for anomaly detection …

Research in anomaly detection suffers from a lack of realistic and publicly-available problem
sets. This paper discusses what properties such problem sets should possess. It then …

Anomaly detection methods identify examples that do not follow the expected behaviour,
typically in an unsupervised fashion, by assigning real-valued anomaly scores to the …

Anomaly detection involves identifying rare data instances (anomalies) that come from a
different class or distribution than the majority (which are simply called “normal” instances) …

In this tutorial we aim to present a comprehensive survey of the advances in deep learning
techniques specifically designed for anomaly detection (deep anomaly detection for short) …

Anomaly detection in videos refers to identifying events that rarely or shouldn't happen in a
certain context. Among all existing methods, the idea of reconstruction or future frame …

Anomaly detection has been widely studied and used in diverse applications. Building an
effective anomaly detection system requires the researchers/developers to learn the …