mlsanityData to 14 Oct 2026

Deep Anomaly Detection Using Geometric Transformations

arXiv 1805.10917 ↗First posted 28 May 2018cs.LG stat.MLcited by 269

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Abstract (arXiv)

We consider the problem of anomaly detection in images, and present a new detection technique. Given a sample of images, all known to belong to a "normal" class (e.g., dogs), we show how to train a deep neural model that can detect out-of-distribution images (i.e., non-dog objects). The main idea behind our scheme is to train a multi-class model to discriminate between dozens of geometric transformations applied on all the given images. The auxiliary expertise learned by the model generates feature detectors that effectively identify, at test time, anomalous images based on the softmax activation statistics of the model when applied on transformed images. We present extensive experiments using the proposed detector, which indicate that our algorithm improves state-of-the-art methods by a wide margin.

Key facts. Cited by 269 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 May 2018.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 74; 2022: 84; 2023: 55; 2024: 24; 2025: 21; 2026: 11.

269 papers.

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  1. 2021Generalized Out-of-Distribution Detection: A Surveycited by 802
  2. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 776
  3. 2021CutPaste: Self-Supervised Learning for Anomaly Detection and Localizationcited by 531
  4. 2022Anomaly Detection via Reverse Distillation from One-Class Embeddingcited by 421
  5. 2022A Unified Model for Multi-class Anomaly Detectioncited by 268
  6. 2021SSD: A Unified Framework for Self-Supervised Outlier Detectioncited by 264
  7. 2021Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learningcited by 230
  8. 2021A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challengescited by 154
  9. 2021Self-Supervised Representation Learning: Introduction, Advances and Challengescited by 148
  10. 2021Student-Teacher Feature Pyramid Matching for Anomaly Detectioncited by 146
  11. 2022Registration based Few-Shot Anomaly Detectioncited by 133
  12. 2021Fully Convolutional Cross-Scale-Flows for Image-based Defect Detectioncited by 117
38 more
  1. 2021Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localizationcited by 116
  2. 2022Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detectioncited by 104
  3. 2021Neural Transformation Learning for Deep Anomaly Detection Beyond Imagescited by 99
  4. 2022Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detectioncited by 93
  5. 2021Mean-Shifted Contrastive Loss for Anomaly Detectioncited by 88
  6. 2021Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detectioncited by 83
  7. 2022Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detectioncited by 80
  8. 2021Explainable Deep Few-shot Anomaly Detection with Deviation Networkscited by 69
  9. 2021Deep One-Class Classification via Interpolated Gaussian Descriptorcited by 67
  10. 2021A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly Detectioncited by 60
  11. 2021NGC: A Unified Framework for Learning with Open-World Noisy Datacited by 58
  12. 2023Anomaly Detection with Conditioned Denoising Diffusion Modelscited by 56
  13. 2022RankFeat: Rank-1 Feature Removal for Out-of-distribution Detectioncited by 52
  14. 2022Latent Outlier Exposure for Anomaly Detection with Contaminated Datacited by 48
  15. 2022GOOD-D: On Unsupervised Graph Out-Of-Distribution Detectioncited by 47
  16. 2023Rethinking Out-of-distribution (OOD) Detection: Masked Image Modeling is All You Needcited by 46
  17. 2021Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Imagescited by 45
  18. 2022Raising the Bar in Graph-level Anomaly Detectioncited by 45
  19. 2021Detecting Outliers with Poisson Image Interpolationcited by 44
  20. 2022Self-Supervised Masking for Unsupervised Anomaly Detection and Localizationcited by 43
  21. 2022Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Seriescited by 42
  22. 2023Towards Self-Interpretable Graph-Level Anomaly Detectioncited by 41
  23. 2022Calibrated One-class Classification for Unsupervised Time Series Anomaly Detectioncited by 40
  24. 2021One-Class Classification: A Surveycited by 35
  25. 2022Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Imagescited by 35
  26. 2021Focus Your Distribution: Coarse-to-Fine Non-Contrastive Learning for Anomaly Detection and Localizationcited by 32
  27. 2022ADTR: Anomaly Detection Transformer with Feature Reconstructioncited by 31
  28. 2024GeneralAD: Anomaly Detection Across Domains by Attending to Distorted Featurescited by 29
  29. 2022Class-Specific Semantic Reconstruction for Open Set Recognitioncited by 28
  30. 2021Corner Cases for Visual Perception in Automated Driving: Some Guidance on Detection Approachescited by 27
  31. 2021OODformer: Out-Of-Distribution Detection Transformercited by 27
  32. 2022iDECODe: In-distribution Equivariance for Conformal Out-of-distribution Detectioncited by 27
  33. 2023Understanding the Feature Norm for Out-of-Distribution Detectioncited by 27
  34. 2025Towards Training-free Anomaly Detection with Vision and Language Foundation Modelscited by 26
  35. 2021DATE: Detecting Anomalies in Text via Self-Supervision of Transformerscited by 25
  36. 2021Taxonomy of Machine Learning Safety: A Survey and Primercited by 25
  37. 2021A Survey on Open Set Recognitioncited by 25
  38. 2021Transformaly -- Two (Feature Spaces) Are Better Than Onecited by 25

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