mlsanityData to 7 Oct 2026

Classification-Based Anomaly Detection for General Data

arXiv 2005.02359 ↗First posted 5 May 2020cs.LG cs.CV stat.MLcited by 210

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Abstract (arXiv)

Anomaly detection, finding patterns that substantially deviate from those seen previously, is one of the fundamental problems of artificial intelligence. Recently, classification-based methods were shown to achieve superior results on this task. In this work, we present a unifying view and propose an open-set method, GOAD, to relax current generalization assumptions. Furthermore, we extend the applicability of transformation-based methods to non-image data using random affine transformations. Our method is shown to obtain state-of-the-art accuracy and is applicable to broad data types. The strong performance of our method is extensively validated on multiple datasets from different domains.

Key facts. Cited by 210 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 May 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 53; 2022: 52; 2023: 43; 2024: 16; 2025: 22; 2026: 24.

210 papers.

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  1. 2021Generalized Out-of-Distribution Detection: A Surveycited by 796
  2. 2021CutPaste: Self-Supervised Learning for Anomaly Detection and Localizationcited by 529
  3. 2021Open-Set Recognition: a Good Closed-Set Classifier is All You Need?cited by 351
  4. 2021SSD: A Unified Framework for Self-Supervised Outlier Detectioncited by 264
  5. 2022ADBench: Anomaly Detection Benchmarkcited by 230
  6. 2021Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learningcited by 226
  7. 2021FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flowscited by 225
  8. 2021A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challengescited by 154
  9. 2022Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detectioncited by 104
  10. 2021Neural Transformation Learning for Deep Anomaly Detection Beyond Imagescited by 98
  11. 2022Deep Isolation Forest for Anomaly Detectioncited by 90
  12. 2024Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Promptscited by 88
38 more
  1. 2021Mean-Shifted Contrastive Loss for Anomaly Detectioncited by 87
  2. 2022Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detectioncited by 79
  3. 2023On Diffusion Modeling for Anomaly Detectioncited by 71
  4. 2021Contrastive Out-of-Distribution Detection for Pretrained Transformerscited by 70
  5. 2021Explainable Deep Few-shot Anomaly Detection with Deviation Networkscited by 69
  6. 2021Deep One-Class Classification via Interpolated Gaussian Descriptorcited by 67
  7. 2021A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly Detectioncited by 60
  8. 2023Unsupervised Out-of-Distribution Detection with Diffusion Inpaintingcited by 57
  9. 2022Latent Outlier Exposure for Anomaly Detection with Contaminated Datacited by 48
  10. 2023Rethinking Out-of-distribution (OOD) Detection: Masked Image Modeling is All You Needcited by 46
  11. 2021Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Imagescited by 45
  12. 2022Calibrated One-class Classification for Unsupervised Time Series Anomaly Detectioncited by 39
  13. 2021Game of Gradients: Mitigating Irrelevant Clients in Federated Learningcited by 35
  14. 2022Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Imagescited by 35
  15. 2021Focus Your Distribution: Coarse-to-Fine Non-Contrastive Learning for Anomaly Detection and Localizationcited by 32
  16. 2021OODformer: Out-Of-Distribution Detection Transformercited by 27
  17. 2022iDECODe: In-distribution Equivariance for Conformal Out-of-distribution Detectioncited by 27
  18. 2021Transformaly -- Two (Feature Spaces) Are Better Than Onecited by 25
  19. 2023Fascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learningcited by 25
  20. 2023Understanding the Feature Norm for Out-of-Distribution Detectioncited by 25
  21. 2023Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detectioncited by 25
  22. 2024MedIAnomaly: A comparative study of anomaly detection in medical imagescited by 25
  23. 2022Perturbation Learning Based Anomaly Detectioncited by 24
  24. 2022Hyperparameter Sensitivity in Deep Outlier Detection: Analysis and a Scalable Hyper-Ensemble Solutioncited by 24
  25. 2021Self-supervised Pseudo Multi-class Pre-training for Unsupervised Anomaly Detection and Segmentation in Medical Imagescited by 23
  26. 2021Taxonomy of Machine Learning Safety: A Survey and Primercited by 22
  27. 2021OpenCoS: Contrastive Semi-supervised Learning for Handling Open-set Unlabeled Datacited by 22
  28. 2024Fine-grained Abnormality Prompt Learning for Zero-shot Anomaly Detectioncited by 22
  29. 2021Self-supervise, Refine, Repeat: Improving Unsupervised Anomaly Detectioncited by 20
  30. 2021Do We Really Need to Learn Representations from In-domain Data for Outlier Detection?cited by 16
  31. 2024Towards Multimodal Open-Set Domain Generalization and Adaptation through Self-supervisioncited by 16
  32. 2022Data-Efficient and Interpretable Tabular Anomaly Detectioncited by 15
  33. 2022Curved Geometric Networks for Visual Anomaly Recognitioncited by 15
  34. 2022Anomaly Detection Requires Better Representationscited by 14
  35. 2023Beyond Individual Input for Deep Anomaly Detection on Tabular Datacited by 14
  36. 2023NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly Generationcited by 14
  37. 2021Learning Augmentation Distributions using Transformed Risk Minimizationcited by 13
  38. 2021Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Typescited by 13

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