mlsanityData to 7 Oct 2026

CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows

arXiv 2107.12571 ↗First posted 27 Jul 2021cs.CV cs.AI cs.LGcited by 320

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Cited by 26 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Unsupervised anomaly detection with localization has many practical applications when labeling is infeasible and, moreover, when anomaly examples are completely missing in the train data. While recently proposed models for such data setup achieve high accuracy metrics, their complexity is a limiting factor for real-time processing. In this paper, we propose a real-time model and analytically derive its relationship to prior methods. Our CFLOW-AD model is based on a conditional normalizing flow framework adopted for anomaly detection with localization. In particular, CFLOW-AD consists of a discriminatively pretrained encoder followed by a multi-scale generative decoders where the latter explicitly estimate likelihood of the encoded features. Our approach results in a computationally and memory-efficient model: CFLOW-AD is faster and smaller by a factor of 10x than prior state-of-the-art with the same input setting. Our experiments on the MVTec dataset show that CFLOW-AD outperforms previous methods by 0.36% AUROC in detection task, by 1.12% AUROC and 2.5% AUPRO in localization task, respectively. We open-source our code with fully reproducible experiments.

Key facts. Cited by 320 papers in the map (data through 7 Oct 2026). Builds on 28 of its 44 references that are in the map. First posted on arXiv 27 Jul 2021.

Builds on

28 of 44 references.

References · 28

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,590
  4. 2016Wide Residual Networkscited by 3,057
  5. 2016Density estimation using Real NVPcited by 2,029
  6. 2019Searching for MobileNetV3cited by 1,984
  7. 2018A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attackscited by 1,472
  8. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,315
  9. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 591
  10. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
  11. 2017Understanding the Effective Receptive Field in Deep Convolutional Neural Networkscited by 549
  12. 2021CutPaste: Self-Supervised Learning for Anomaly Detection and Localizationcited by 529
16 more
  1. 2018Do Deep Generative Models Know What They Don't Know?cited by 443
  2. 2020A Unifying Review of Deep and Shallow Anomaly Detectioncited by 423
  3. 2019Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddingscited by 386
  4. 2020Sub-Image Anomaly Detection with Deep Pyramid Correspondencescited by 297
  5. 2020Multiresolution Knowledge Distillation for Anomaly Detectioncited by 285
  6. 2018Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoderscited by 263
  7. 2018Analyzing Inverse Problems with Invertible Neural Networkscited by 239
  8. 2020Why Normalizing Flows Fail to Detect Out-of-Distribution Datacited by 220
  9. 2020Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentationcited by 212
  10. 2020Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flowscited by 212
  11. 2019Guided Image Generation with Conditional Invertible Neural Networkscited by 147
  12. 2018Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Imagescited by 146
  13. 2020Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly Detectioncited by 115
  14. 2020Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Featurescited by 64
  15. 2020Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal Imagescited by 52
  16. 2018q-Space Novelty Detection with Variational Autoencoderscited by 22

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 35; 2023: 71; 2024: 84; 2025: 68; 2026: 59.

320 papers.

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  1. 2023SimpleNet: A Simple Network for Image Anomaly Detection and Localizationcited by 256
  2. 2021FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flowscited by 225
  3. 2023AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Modelscited by 169
  4. 2023EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latenciescited by 157
  5. 2022DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detectioncited by 153
  6. 2023Deep Industrial Image Anomaly Detection: A Surveycited by 147
  7. 2023Multimodal Industrial Anomaly Detection via Hybrid Fusioncited by 138
  8. 2024Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detectioncited by 138
  9. 2022CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly Localizationcited by 135
  10. 2022Registration based Few-Shot Anomaly Detectioncited by 133
  11. 2021Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localizationcited by 116
  12. 2024RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detectioncited by 116
38 more
  1. 2022Asymmetric Student-Teacher Networks for Industrial Anomaly Detectioncited by 115
  2. 2023AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Modelcited by 110
  3. 2023BMAD: Benchmarks for Medical Anomaly Detectioncited by 81
  4. 2022Anomalib: A Deep Learning Library for Anomaly Detectioncited by 75
  5. 2023PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flowcited by 74
  6. 2023Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detectioncited by 74
  7. 2024A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localizationcited by 74
  8. 2022Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detectioncited by 72
  9. 2025AA-CLIP: Enhancing Zero-shot Anomaly Detection via Anomaly-Aware CLIPcited by 72
  10. 2022PNI : Industrial Anomaly Detection using Position and Neighborhood Informationcited by 69
  11. 2024A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospectcited by 69
  12. 2024Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Imagescited by 69
  13. 2022Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Surveycited by 67
  14. 2023Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Networkcited by 67
  15. 2022Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptorscited by 66
  16. 2023Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detectioncited by 66
  17. 2023IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturingcited by 64
  18. 2023ReContrast: Domain-Specific Anomaly Detection via Contrastive Reconstructioncited by 62
  19. 2022Prototypical Residual Networks for Anomaly Detection and Localizationcited by 56
  20. 2023Collaborative Discrepancy Optimization for Reliable Image Anomaly Localizationcited by 56
  21. 2023Anomaly Detection with Conditioned Denoising Diffusion Modelscited by 56
  22. 2024SoftPatch: Unsupervised Anomaly Detection with Noisy Datacited by 55
  23. 2023Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detectioncited by 47
  24. 2023DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detectioncited by 46
  25. 2023PAD: A Dataset and Benchmark for Pose-agnostic Anomaly Detectioncited by 46
  26. 2024R3D-AD: Reconstruction via Diffusion for 3D Anomaly Detectioncited by 45
  27. 2023EasyNet: An Easy Network for 3D Industrial Anomaly Detectioncited by 43
  28. 2023TransFusion -- A Transparency-Based Diffusion Model for Anomaly Detectioncited by 43
  29. 2023Multimodal Industrial Anomaly Detection by Crossmodal Feature Mappingcited by 41
  30. 2022Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Imagescited by 35
  31. 2023Cheating Depth: Enhancing 3D Surface Anomaly Detection via Depth Simulationcited by 33
  32. 2023DiAD: A Diffusion-based Framework for Multi-class Anomaly Detectioncited by 33
  33. 2023Focus the Discrepancy: Intra- and Inter-Correlation Learning for Image Anomaly Detectioncited by 32
  34. 2024Unsupervised Continual Anomaly Detection with Contrastively-learned Promptcited by 31
  35. 2022MAEDAY: MAE for few and zero shot AnomalY-Detectioncited by 30
  36. 2023ReConPatch : Contrastive Patch Representation Learning for Industrial Anomaly Detectioncited by 30
  37. 2024PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detectioncited by 30
  38. 2024Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detectioncited by 30

50 most cited of 320 All 320 (JSON)