mlsanityData to 7 Oct 2026

Weakly Supervised Anomaly Detection: A Survey

arXiv 2302.04549 ↗First posted 9 Feb 2023cs.LG cs.AIcited by 22

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Abstract (arXiv)

Anomaly detection (AD) is a crucial task in machine learning with various applications, such as detecting emerging diseases, identifying financial frauds, and detecting fake news. However, obtaining complete, accurate, and precise labels for AD tasks can be expensive and challenging due to the cost and difficulties in data annotation. To address this issue, researchers have developed AD methods that can work with incomplete, inexact, and inaccurate supervision, collectively summarized as weakly supervised anomaly detection (WSAD) methods. In this study, we present the first comprehensive survey of WSAD methods by categorizing them into the above three weak supervision settings across four data modalities (i.e., tabular, graph, time-series, and image/video data). For each setting, we provide formal definitions, key algorithms, and potential future directions. To support future research, we conduct experiments on a selected setting and release the source code, along with a collection of WSAD methods and data.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Builds on 33 of its 74 references that are in the map. First posted on arXiv 9 Feb 2023.

Builds on

33 of 74 references.

References · 33

  1. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  2. 2022FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecastingcited by 1,200
  3. 2016Temporal Ensembling for Semi-Supervised Learningcited by 1,054
  4. 2018Real-world Anomaly Detection in Surveillance Videoscited by 570
  5. 2021CutPaste: Self-Supervised Learning for Anomaly Detection and Localizationcited by 529
  6. 2016Adversarial Feature Learningcited by 350
  7. 2019Deep Semi-Supervised Anomaly Detectioncited by 290
  8. 2022ADBench: Anomaly Detection Benchmarkcited by 230
  9. 2021Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learningcited by 226
  10. 2019PyOD: A Python Toolbox for Scalable Outlier Detectioncited by 221
  11. 2022TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Datacited by 211
  12. 2019Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detectioncited by 171
21 more
  1. 2016Multiple Instance Learning: A Survey of Problem Characteristics and Applicationscited by 163
  2. 2019Deep Anomaly Detection with Deviation Networkscited by 152
  3. 2020A Semi-supervised Graph Attentive Network for Financial Fraud Detectioncited by 150
  4. 2021MIST: Multiple Instance Self-Training Framework for Video Anomaly Detectioncited by 137
  5. 2022BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphscited by 80
  6. 2014Toward Supervised Anomaly Detectioncited by 73
  7. 2021Few-shot Network Anomaly Detection via Cross-network Meta-learningcited by 64
  8. 2019Deep Weakly-supervised Anomaly Detectioncited by 59
  9. 2019Motion-Aware Feature for Improved Video Anomaly Detectioncited by 48
  10. 2021Weakly Supervised Video Anomaly Detection via Center-guided Discriminative Learningcited by 44
  11. 2019XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learningcited by 42
  12. 2020Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly Datacited by 42
  13. 2021Feature Encoding with AutoEncoders for Weakly-supervised Anomaly Detectioncited by 42
  14. 2020Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learningcited by 34
  15. 2021Weakly-Supervised Spatio-Temporal Anomaly Detection in Surveillance Videocited by 32
  16. 2020A Self-Reasoning Framework for Anomaly Detection Using Video-Level Labelscited by 23
  17. 2020Semi-supervised Anomaly Detection on Attributed Graphscited by 18
  18. 2022ADMoE: Anomaly Detection with Mixture-of-Experts from Noisy Labelscited by 15
  19. 2022Anomaly Detection by Leveraging Incomplete Anomalous Knowledge with Anomaly-Aware Bidirectional GANscited by 11
  20. 2021Weakly Supervised Temporal Anomaly Segmentation with Dynamic Time Warpingcited by 7
  21. 2019Anomaly Detection with Inexact Labelscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 7; 2024: 8; 2025: 4; 2026: 3.

22 papers.

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  1. 2023Data-centric Artificial Intelligence: A Surveycited by 202
  2. 2023GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detectioncited by 57
  3. 2023Vision-Based Traffic Accident Detection and Anticipation: A Surveycited by 31
  4. 2024Video Anomaly Detection in 10 Years: A Survey and Outlookcited by 18
  5. 2023RoSAS: Deep Semi-Supervised Anomaly Detection with Contamination-Resilient Continuous Supervisioncited by 11
  6. 2023ADGym: Design Choices for Deep Anomaly Detectioncited by 11
  7. 2023Open-Set Graph Anomaly Detection via Normal Structure Regularisationcited by 11
  8. 2024Weakly Supervised Anomaly Detection via Knowledge-Data Alignmentcited by 10
  9. 2026Hide-and-Seek in Trajectories: Discovering Failure Signals for VLA Runtime Monitoringcited by 6
  10. 2025Building Machine Learning Challenges for Anomaly Detection in Sciencecited by 5
  11. 2025The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLMcited by 4
  12. 2023AnoOnly: Semi-Supervised Anomaly Detection with the Only Loss on Anomaliescited by 1
10 more
  1. 2024TreeMIL: A Multi-instance Learning Framework for Time Series Anomaly Detection with Inexact Supervisioncited by 1
  2. 2024Multi-Normal Prototypes Learning for Weakly Supervised Anomaly Detectioncited by 1
  3. 2024Identifying Spatio-Temporal Drivers of Extreme Eventscited by 1
  4. 2024Weakly-supervised anomaly detection for multimodal data distributionscited by 0
  5. 2024Balanced Edge Pruning for Graph Anomaly Detection with Noisy Labelscited by 0
  6. 2024Class-aware and Augmentation-free Contrastive Learning from Label Proportioncited by 0
  7. 2025Explainable Deep Convolutional Multi-Type Anomaly Detectioncited by 0
  8. 2025Label-Informed Outlier Detection Based on Granule Densitycited by 0
  9. 2026Seeing the Needle in the Haystack: Towards Weakly-Supervised Log Instance Anomaly Localization via Counterfactual Perturbationcited by 0
  10. 2026Learning Feature Encoder with Synthetic Anomalies for Weakly Supervised Graph Anomaly Detectioncited by 0