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Full-Text Articles in Physical Sciences and Mathematics
Andea: Anomaly And Novelty Detection, Explanation, And Accommodation, Guansong Pang, Jundong Li, Anton Van Den Hengel, Longbin Cao, Thomas G. Dietterich
Andea: Anomaly And Novelty Detection, Explanation, And Accommodation, Guansong Pang, Jundong Li, Anton Van Den Hengel, Longbin Cao, Thomas G. Dietterich
Research Collection School Of Computing and Information Systems
The detection of, explanation of, and accommodation to anomalies and novelties are active research areas in multiple communities, including data mining, machine learning, and computer vision. They are applied in various guises including anomaly detection, out-of-distribution example detection, adversarial example recognition and detection, curiosity-driven reinforcement learning, and open-set recognition and adaptation, all of which are of great interest to the SIGKDD community. The techniques developed have been applied in a wide range of domains including fraud detection and anti-money laundering in fintech, early disease detection, intrusion detection in large-scale computer networks and data centers, defending AI systems from adversarial attacks, …
Catching Both Gray And Black Swans: Open-Set Supervised Anomaly Detection, Choubo Ding, Guansong Pang, Chunhua Shen
Catching Both Gray And Black Swans: Open-Set Supervised Anomaly Detection, Choubo Ding, Guansong Pang, Chunhua Shen
Research Collection School Of Computing and Information Systems
Despite most existing anomaly detection studies assume the availability of normal training samples only, a few labeled anomaly examples are often available in many real-world applications, such as defect samples identified during random quality inspection, lesion images confirmed by radiologists in daily medical screening, etc. These anomaly examples provide valuable knowledge about the application-specific abnormality, enabling significantly improved detection of similar anomalies in some recent models. However, those anomalies seen during training often do not illustrate every possible class of anomaly, rendering these models ineffective in generalizing to unseen anomaly classes. This paper tackles open-set supervised anomaly detection, in which …
Deep Learning For Anomaly Detection, Guansong Pang, Charu Aggarwal, Chunhua Shen, Nicu Sebe
Deep Learning For Anomaly Detection, Guansong Pang, Charu Aggarwal, Chunhua Shen, Nicu Sebe
Research Collection School Of Computing and Information Systems
A nomaly detection aims at identifying data points which are rare or significantly different from the majority of data points. Many techniques are explored to build highly efficient and effective anomaly detection systems, but they are confronted with many difficulties when dealing with complex data, such as failing to capture intricate feature interactions or extract good feature representations. Deep-learning techniques have shown very promising performance in tackling different types of complex data in a broad range of tasks/problems, including anomaly detection. To address this new trend, we organized this Special Issue on Deep Learning for Anomaly Detection to cover the …
Deep Learning For Anomaly Detection: A Review, Guansong Pang, Chunhua Shen, Longbing Cao, Anton Van Den Hengel
Deep Learning For Anomaly Detection: A Review, Guansong Pang, Chunhua Shen, Longbing Cao, Anton Van Den Hengel
Research Collection School Of Computing and Information Systems
Anomaly detection, a.k.a. outlier detection or novelty detection, has been a lasting yet active research area in various research communities for several decades. There are still some unique problem complexities and challenges that require advanced approaches. In recent years, deep learning enabled anomaly detection, i.e., deep anomaly detection, has emerged as a critical direction. This article surveys the research of deep anomaly detection with a comprehensive taxonomy, covering advancements in 3 high-level categories and 11 fine-grained categories of the methods. We review their key intuitions, objective functions, underlying assumptions, advantages, and disadvantages and discuss how they address the aforementioned challenges. …