Data to 7 Oct 2026

Deep Learning for Anomaly Detection: A Survey

arXiv 1901.03407 ↗First posted 10 Jan 2019cs.LG stat.MLcited by 357

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Abstract (arXiv)

Anomaly detection is an important problem that has been well-studied within diverse research areas and application domains. The aim of this survey is two-fold, firstly we present a structured and comprehensive overview of research methods in deep learning-based anomaly detection. Furthermore, we review the adoption of these methods for anomaly across various application domains and assess their effectiveness. We have grouped state-of-the-art research techniques into different categories based on the underlying assumptions and approach adopted. Within each category we outline the basic anomaly detection technique, along with its variants and present key assumptions, to differentiate between normal and anomalous behavior. For each category, we present we also present the advantages and limitations and discuss the computational complexity of the techniques in real application domains. Finally, we outline open issues in research and challenges faced while adopting these techniques.

Key facts. Cited by 357 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Jan 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 90; 2022: 77; 2023: 74; 2024: 47; 2025: 33; 2026: 36.

357 papers.

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2021Generalized Out-of-Distribution Detection: A Surveycited by 796
2021A Comprehensive Survey on Graph Anomaly Detection with Deep Learningcited by 230
2022TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Datacited by 211
2021Trustworthy AI: From Principles to Practicescited by 206
2021A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challengescited by 154
2021Testing for Outliers with Conformal p-valuescited by 133
2022Deep Learning for Time Series Anomaly Detection: A Surveycited by 130
2022Generative Cooperative Learning for Unsupervised Video Anomaly Detectioncited by 102
2021Towards a Rigorous Evaluation of Time-series Anomaly Detectioncited by 95
2022MGFN: Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly Detectioncited by 87
2022Training OOD Detectors in their Natural Habitatscited by 86
2021An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Seriescited by 68

38 more

2022Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Surveycited by 67
2021Error-Aware Imitation Learning from Teleoperation Data for Mobile Manipulationcited by 59
2022Rethinking Reconstruction Autoencoder-Based Out-of-Distribution Detectioncited by 59
2021A Dynamic Spatial-temporal Attention Network for Early Anticipation of Traffic Accidentscited by 42
2021Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signalscited by 41
2023Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Leadcited by 40
2022A Survey on Explainable Anomaly Detectioncited by 39
2024Towards a General Time Series Anomaly Detector with Adaptive Bottlenecks and Dual Adversarial Decoderscited by 37
2024Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Surveycited by 36
2021One-Class Classification: A Surveycited by 35
2022Signature Methods in Machine Learningcited by 33
2021Autoencoders for unsupervised anomaly detection in high energy physicscited by 32
2021Deep Learning-based Action Detection in Untrimmed Videos: A Surveycited by 32
2021Time Series Anomaly Detection for Cyber-Physical Systems via Neural System Identification and Bayesian Filteringcited by 31
2023A Survey on Out-of-Distribution Detection in NLPcited by 31
2022On the Importance of Building High-quality Training Datasets for Neural Code Searchcited by 30
2024GeneralAD: Anomaly Detection Across Domains by Attending to Distorted Featurescited by 29
2021A Survey on Anomaly Detection for Technical Systems using LSTM Networkscited by 27
2022Deep Learning for Anomaly Detection in Log Data: A Surveycited by 27
2023LogGPT: Log Anomaly Detection via GPTcited by 26
2023VISION Datasets: A Benchmark for Vision-based InduStrial InspectiONcited by 25
2022Hyperparameter Sensitivity in Deep Outlier Detection: Analysis and a Scalable Hyper-Ensemble Solutioncited by 24
2022Graph Anomaly Detection with Graph Neural Networks: Current Status and Challengescited by 23
2021Distribution-Aware Testing of Neural Networks Using Generative Modelscited by 22
2021Regularity Learning via Explicit Distribution Modeling for Skeletal Video Anomaly Detectioncited by 22
2023A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directionscited by 22
2021DASVDD: Deep Autoencoding Support Vector Data Descriptor for Anomaly Detectioncited by 21
2022Detecting Multivariate Time Series Anomalies with Zero Known Labelcited by 21
2022A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systemscited by 20
2022DAGAD: Data Augmentation for Graph Anomaly Detectioncited by 20
2022Cross-Domain Video Anomaly Detection without Target Domain Adaptationcited by 20
2023MST-GAT: A Multimodal Spatial-Temporal Graph Attention Network for Time Series Anomaly Detectioncited by 20
2024See it, Think it, Sorted: Large Multimodal Models are Few-shot Time Series Anomaly Analyzerscited by 20
2021Adjusting for Autocorrelated Errors in Neural Networks for Time Seriescited by 18
2023Toward Unsupervised 3D Point Cloud Anomaly Detection using Variational Autoencodercited by 18
2023Contextual Affinity Distillation for Image Anomaly Detectioncited by 18
2024Video Anomaly Detection in 10 Years: A Survey and Outlookcited by 18
2024Anomaly Detection in Dynamic Graphs: A Comprehensive Surveycited by 18

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