Deep Learning for Anomaly Detection: A Review

Abstract

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 paper surveys the research of deep anomaly detection with a comprehensive taxonomy, covering advancements in three 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. We further discuss a set of possible future opportunities and new perspectives on addressing the challenges.

Survey paper, 36 pages, 180 references, 2 figures, 4 tables

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Cited by in corpus (115)

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• CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly Detection
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• Self-Supervised Anomaly Detection in Computer Vision and Beyond: A Survey and Outlook
• Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation
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• Lifelong Continual Learning for Anomaly Detection: New Challenges, Perspectives, and Insights
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• ALGAN: Anomaly Detection by Generating Pseudo Anomalous Data via Latent Variables
• Unsupervised Graph Outlier Detection: Problem Revisit, New Insight, and Superior Method
• Anomaly detection and community detection in networks
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• GRAM: An Interpretable Approach for Graph Anomaly Detection using Gradient Attention Maps
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• Black-box Error Diagnosis in Deep Neural Networks for Computer Vision: a Survey of Tools
• Convolutional Neural Network Design and Evaluation for Real-Time Multivariate Time Series Fault Detection in Spacecraft Attitude Sensors
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• Lessons from the Development of an Anomaly Detection Interface on the Mars Perseverance Rover using the ISHMAP Framework
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• Quantum Patch-Based Autoencoder for Anomaly Segmentation
• OrionBench: Benchmarking Time Series Generative Models in the Service of the End-User
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