mlsanityData to 7 Oct 2026

Deep Learning for Anomaly Detection: A Review

arXiv 2007.02500 ↗First posted 6 Jul 2020cs.LG cs.CV stat.MLcited by 159

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 159 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Jul 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 13; 2022: 34; 2023: 44; 2024: 33; 2025: 29; 2026: 6.

159 papers.

Sort:

By area:

  1. 2021Trustworthy AI: From Principles to Practicescited by 206
  2. 2023EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latenciescited by 157
  3. 2022Deep Learning for Time Series Anomaly Detection: A Surveycited by 130
  4. 2023A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validationcited by 91
  5. 2023Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Modelscited by 59
  6. 2022Prototypical Residual Networks for Anomaly Detection and Localizationcited by 56
  7. 2021Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Imagescited by 45
  8. 2022Machine Learning in Aerodynamic Shape Optimizationcited by 39
  9. 2022A Survey on Explainable Anomaly Detectioncited by 39
  10. 2022Predicting the Future of AI with AI: High-quality link prediction in an exponentially growing knowledge networkcited by 34
  11. 2021Autoencoders for unsupervised anomaly detection in high energy physicscited by 32
  12. 2023LogGPT: Log Anomaly Detection via GPTcited by 26
38 more
  1. 2024MedIAnomaly: A comparative study of anomaly detection in medical imagescited by 25
  2. 2022Self-Supervised Anomaly Detection in Computer Vision and Beyond: A Survey and Outlookcited by 24
  3. 2024The Clever Hans Effect in Unsupervised Learningcited by 24
  4. 2021Self-supervised Pseudo Multi-class Pre-training for Unsupervised Anomaly Detection and Segmentation in Medical Imagescited by 23
  5. 2022AI Autonomy : Self-Initiated Open-World Continual Learning and Adaptationcited by 23
  6. 2023Open-World Continual Learning: Unifying Novelty Detection and Continual Learningcited by 22
  7. 2022A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Imagescited by 21
  8. 2022Trustworthy Recommender Systemscited by 21
  9. 2023Deep Learning in Deterministic Computational Mechanicscited by 21
  10. 2024Networking Systems for Video Anomaly Detection: A Tutorial and Surveycited by 21
  11. 2022Cross-Domain Video Anomaly Detection without Target Domain Adaptationcited by 20
  12. 2021Deep Clustering based Fair Outlier Detectioncited by 19
  13. 2022Toward Unsupervised Outlier Model Selectioncited by 18
  14. 2024Video Anomaly Detection in 10 Years: A Survey and Outlookcited by 18
  15. 2022Astronomia ex machina: a history, primer, and outlook on neural networks in astronomycited by 17
  16. 2022Unsupervised Graph Outlier Detection: Problem Revisit, New Insight, and Superior Methodcited by 15
  17. 2023CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly Detectioncited by 15
  18. 2022Anatomy-aware Self-supervised Learning for Anomaly Detection in Chest Radiographscited by 12
  19. 2023Meta-survey on outlier and anomaly detectioncited by 12
  20. 2023RoSAS: Deep Semi-Supervised Anomaly Detection with Contamination-Resilient Continuous Supervisioncited by 11
  21. 2023Time Series Anomaly Detection using Diffusion-based Modelscited by 11
  22. 2022Functional Anomaly Detection: a Benchmark Studycited by 10
  23. 2022Fast Deep Autoencoder for Federated learningcited by 10
  24. 2022A Comparative Study on Unsupervised Anomaly Detection for Time Series: Experiments and Analysiscited by 10
  25. 2023Explainable Contextual Anomaly Detection using Quantile Regression Forestscited by 10
  26. 2023Survey on video anomaly detection in dynamic scenes with moving camerascited by 10
  27. 2024Anomaly Detection Based on Isolation Mechanisms: A Surveycited by 10
  28. 2021TIMo -- A Dataset for Indoor Building Monitoring with a Time-of-Flight Cameracited by 9
  29. 2021A Novel Intrinsic Measure of Data Separabilitycited by 9
  30. 2023REB: Reducing Biases in Representation for Industrial Anomaly Detectioncited by 9
  31. 2023Data Cleaning and Machine Learning: A Systematic Literature Reviewcited by 9
  32. 2023PseudoBound: Limiting the anomaly reconstruction capability of one-class classifiers using pseudo anomaliescited by 8
  33. 2023Graph Neural Networks based Log Anomaly Detection and Explanationcited by 8
  34. 2023Successive Data Injection in Conditional Quantum GAN Applied to Time Series Anomaly Detectioncited by 8
  35. 2023UADB: Unsupervised Anomaly Detection Boostercited by 7
  36. 2024Data-driven Machinery Fault Diagnosis: A Comprehensive Reviewcited by 7
  37. 2024The OPS-SAT benchmark for detecting anomalies in satellite telemetrycited by 7
  38. 2022PAEDID: Patch Autoencoder Based Deep Image Decomposition For Pixel-level Defective Region Segmentationcited by 6

50 most cited of 159 All 159 (JSON)