mlsanityData to 14 Oct 2026

Self-Supervised Anomaly Detection in Computer Vision and Beyond: A Survey and Outlook

arXiv 2205.05173 ↗First posted 10 May 2022cs.LGcited by 24

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Abstract (arXiv)

Anomaly detection (AD) plays a crucial role in various domains, including cybersecurity, finance, and healthcare, by identifying patterns or events that deviate from normal behaviour. In recent years, significant progress has been made in this field due to the remarkable growth of deep learning models. Notably, the advent of self-supervised learning has sparked the development of novel AD algorithms that outperform the existing state-of-the-art approaches by a considerable margin. This paper aims to provide a comprehensive review of the current methodologies in self-supervised anomaly detection. We present technical details of the standard methods and discuss their strengths and drawbacks. We also compare the performance of these models against each other and other state-of-the-art anomaly detection models. Finally, the paper concludes with a discussion of future directions for self-supervised anomaly detection, including the development of more effective and efficient algorithms and the integration of these techniques with other related fields, such as multi-modal learning.

Key facts. Cited by 24 papers in the map (data through 14 Oct 2026). Builds on 41 of its 91 references that are in the map. First posted on arXiv 10 May 2022.

Builds on

41 of 91 references.

References · 41

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29 more
  1. 2020A Unifying Review of Deep and Shallow Anomaly Detectioncited by 425
  2. 2018GANomaly: Semi-Supervised Anomaly Detection via Adversarial Trainingcited by 409
  3. 2020Debiased Contrastive Learningcited by 356
  4. 2021Big Self-Supervised Models Advance Medical Image Classificationcited by 320
  5. 2021Graph Self-Supervised Learning: A Surveycited by 289
  6. 2018Deep Anomaly Detection Using Geometric Transformationscited by 269
  7. 2021SSD: A Unified Framework for Self-Supervised Outlier Detectioncited by 264
  8. 2021Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learningcited by 177
  9. 2020Deep Learning for Anomaly Detection: A Reviewcited by 159
  10. 2020Multi-task self-supervised learning for Robust Speech Recognitioncited by 130
  11. 2020Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly Detectioncited by 115
  12. 2021Neural Transformation Learning for Deep Anomaly Detection Beyond Imagescited by 99
  13. 2021Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detectioncited by 67
  14. 2021Anomaly Detection in Dynamic Graphs via Transformercited by 59
  15. 2020DFR: Deep Feature Reconstruction for Unsupervised Anomaly Segmentationcited by 54
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  20. 2018Open Category Detection with PAC Guaranteescited by 35
  21. 2021Hop-Count Based Self-Supervised Anomaly Detection on Attributed Networkscited by 31
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  25. 2018Phonocardiographic Sensing using Deep Learning for Abnormal Heartbeat Detectioncited by 15
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 5; 2025: 11; 2026: 6.

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  1. 2024Networking Systems for Video Anomaly Detection: A Tutorial and Surveycited by 21
  2. 2023Graph Anomaly Detection in Time Series: A Surveycited by 18
  3. 2025Deep Learning Advancements in Anomaly Detection: A Comprehensive Surveycited by 17
  4. 2024VMAD: Visual-enhanced Multimodal Large Language Model for Zero-Shot Anomaly Detectioncited by 13
  5. 2023Anomalies, Representations, and Self-Supervisioncited by 11
  6. 2024Graph-Jigsaw Conditioned Diffusion Model for Skeleton-based Video Anomaly Detectioncited by 9
  7. 2025Anomaly Detection and Generation with Diffusion Models: A Surveycited by 8
  8. 2025Foundation Models and Transformers for Anomaly Detection: A Surveycited by 8
  9. 2025SelfMAD: Enhancing Generalization and Robustness in Morphing Attack Detection via Self-Supervised Learningcited by 7
  10. 2024Unveiling the Flaws: A Critical Analysis of Initialization Effect on Time Series Anomaly Detectioncited by 2
  11. 2025ISP-AD: A Large-Scale Real-World Dataset for Advancing Industrial Anomaly Detection with Synthetic and Real Defectscited by 2
  12. 2026TinyGLASS: Real-Time Self-Supervised In-Sensor Anomaly Detectioncited by 2
12 more
  1. 2024Why CNN Features Are not Gaussian: A Statistical Anatomy of Deep Representationscited by 1
  2. 2025Zero-Shot Visual Deepfake Detection: Can AI Predict and Prevent Fake Content Before It's Created?cited by 1
  3. 2025SSL-SE-EEG: A Framework for Robust Learning from Unlabeled EEG Data with Self-Supervised Learning and Squeeze-Excitation Networkscited by 1
  4. 2026Benchmarking Compact VLMs for Clip-Level Surveillance Anomaly Detection Under Weak Supervisioncited by 1
  5. 2025Domain and Task-Focused Example Selection for Data-Efficient Contrastive Medical Image Segmentationcited by 0
  6. 2025Towards Generalized Range-View LiDAR Segmentation in Adverse Weathercited by 0
  7. 2025Anomalous Samples for Few-Shot Anomaly Detectioncited by 0
  8. 2025Rare anomalies require large datasets: About proving the existence of anomaliescited by 0
  9. 2026BAAF: Universal Transformation of One-Class Classifiers for Unsupervised Image Anomaly Detectioncited by 0
  10. 2026EngineAD: A Real-World Vehicle Engine Anomaly Detection Datasetcited by 0
  11. 2026ProtoX-AD: Self-Explainable Time Series Anomaly Detection and Characterizationcited by 0
  12. 2026Feature Transformation Enhanced Jacobi Polynomial Graph Filtering for Graph Anomaly Detectioncited by 0