mlsanityData to 14 Oct 2026

Benchmarking Unsupervised Anomaly Detection and Localization

arXiv 2205.14852 ↗First posted 30 May 2022cs.CVcited by 11

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Abstract (arXiv)

Unsupervised anomaly detection and localization, as of one the most practical and challenging problems in computer vision, has received great attention in recent years. From the time the MVTec AD dataset was proposed to the present, new research methods that are constantly being proposed push its precision to saturation. It is the time to conduct a comprehensive comparison of existing methods to inspire further research. This paper extensively compares 13 papers in terms of the performance in unsupervised anomaly detection and localization tasks, and adds a comparison of inference efficiency previously ignored by the community. Meanwhile, analysis of the MVTec AD dataset are also given, especially the label ambiguity that affects the model fails to achieve full marks. Moreover, considering the proposal of the new MVTec 3D-AD dataset, this paper also conducts experiments using the existing state-of-the-art 2D methods on this new dataset, and reports the corresponding results with analysis.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 32 of its 38 references that are in the map. First posted on arXiv 30 May 2022.

Builds on

32 of 38 references.

References · 32

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  7. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
  8. 2019Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detectioncited by 564
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  11. 2022Anomaly Detection via Reverse Distillation from One-Class Embeddingcited by 421
  12. 2018GANomaly: Semi-Supervised Anomaly Detection via Adversarial Trainingcited by 409
20 more
  1. 2019Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddingscited by 386
  2. 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 321
  3. 2020Sub-Image Anomaly Detection with Deep Pyramid Correspondencescited by 300
  4. 2018Deep Anomaly Detection Using Geometric Transformationscited by 269
  5. 2021FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flowscited by 227
  6. 2020Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentationcited by 212
  7. 2020Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flowscited by 212
  8. 2020Classification-Based Anomaly Detection for General Datacited by 211
  9. 2018Adversarially Learned One-Class Classifier for Novelty Detectioncited by 202
  10. 2021The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localizationcited by 178
  11. 2020PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentationcited by 175
  12. 2020Learning and Evaluating Representations for Deep One-class Classificationcited by 133
  13. 2018Efficient GAN-Based Anomaly Detectioncited by 122
  14. 2021Fully Convolutional Cross-Scale-Flows for Image-based Defect Detectioncited by 117
  15. 2020Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly Detectioncited by 115
  16. 2019Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detectioncited by 100
  17. 2018Generative Probabilistic Novelty Detection with Adversarial Autoencoderscited by 99
  18. 2020DFR: Deep Feature Reconstruction for Unsupervised Anomaly Segmentationcited by 54
  19. 2021Focus Your Distribution: Coarse-to-Fine Non-Contrastive Learning for Anomaly Detection and Localizationcited by 32
  20. 2016Identifying and Categorizing Anomalies in Retinal Imaging Datacited by 18

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Cited by year

Citing papers per year: 2022: 2; 2023: 4; 2024: 4; 2025: 1.

11 papers.

  1. 2022ADBench: Anomaly Detection Benchmarkcited by 232
  2. 2023Multimodal Industrial Anomaly Detection via Hybrid Fusioncited by 139
  3. 2023BMAD: Benchmarks for Medical Anomaly Detectioncited by 82
  4. 2023IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturingcited by 64
  5. 2024M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via Multimodal Denoisingcited by 24
  6. 2022Reconstruction from edge image combined with color and gradient difference for industrial surface anomaly detectioncited by 18
  7. 2024Towards Zero-shot 3D Anomaly Localizationcited by 8
  8. 2024Uni-3DAD: GAN-Inversion Aided Universal 3D Anomaly Detection on Model-free Productscited by 5
  9. 2023FAIR: Frequency-aware Image Restoration for Industrial Visual Anomaly Detectioncited by 1
  10. 2024Leveraging Unsupervised Learning for Cost-Effective Visual Anomaly Detectioncited by 1
  11. 2025On-Device Continual Learning for Unsupervised Visual Anomaly Detection in Dynamic Manufacturingcited by 0