Unsupervised anomaly detection aims to identify data samples that have low probability
density from a set of input samples, and only the normal samples are provided for model …

Anomaly detection in high-dimensional time series is typically tackled using either
reconstruction-or forecasting-based algorithms due to their abilities to learn compressed …

Abstract Unsupervised image Anomaly Detection (UAD) aims to learn robust and
discriminative representations of normal samples. While separate solutions per class endow …

Nearest neighbors is a successful and long-standing technique for anomaly detection.
Significant progress has been recently achieved by self-supervised deep methods (eg …

Learning to reject is a special kind of self-awareness (the ability to know what you do not
know), which is an essential factor for humans to become smarter. Although machine …

Recently, anomaly detection and localization in multimedia data have received significant
attention among the machine learning community. In real-world applications such as …

This survey presents a comprehensive overview of machine learning methods for
cybersecurity intrusion detection systems, with a specific focus on recent approaches based …

As robots acquire increasingly sophisticated skills and see increasingly complex and varied
environments, the threat of an edge case or anomalous failure is ever present. For example …

As the labeled anomalous medical images are usually difficult to acquire, especially for rare
diseases, the deep learning based methods, which heavily rely on the large amount of …

Intrusion detection is a key topic in cybersecurity. It aims to protect computer systems and
networks from intruders and malicious attacks. Traditional intrusion detection systems (IDS) …