mlsanityData to 14 Oct 2026

FAPM: Fast Adaptive Patch Memory for Real-time Industrial Anomaly Detection

arXiv 2211.07381 ↗First posted 14 Nov 2022cs.CVcited by 16

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Abstract (arXiv)

Feature embedding-based methods have shown exceptional performance in detecting industrial anomalies by comparing features of target images with normal images. However, some methods do not meet the speed requirements of real-time inference, which is crucial for real-world applications. To address this issue, we propose a new method called Fast Adaptive Patch Memory (FAPM) for real-time industrial anomaly detection. FAPM utilizes patch-wise and layer-wise memory banks that store the embedding features of images at the patch and layer level, respectively, which eliminates unnecessary repetitive computations. We also propose patch-wise adaptive coreset sampling for faster and more accurate detection. FAPM performs well in both accuracy and speed compared to other state-of-the-art methods

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). Builds on 13 of its 24 references that are in the map. First posted on arXiv 14 Nov 2022.

Builds on

13 of 24 references.

References · 13

  1. 2016Wide Residual Networkscited by 3,070
  2. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 776
  3. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 594
  4. 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 321
  5. 2020Sub-Image Anomaly Detection with Deep Pyramid Correspondencescited by 300
  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. 2019Towards Visually Explaining Variational Autoencoderscited by 113
  9. 2021Inpainting Transformer for Anomaly Detectioncited by 91
  10. 2021Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detectioncited by 83
  11. 2022Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Surveycited by 68
  12. 2019Small-GAN: Speeding Up GAN Training Using Core-setscited by 41
1 more
  1. 2020TrustMAE: A Noise-Resilient Defect Classification Framework using Memory-Augmented Auto-Encoders with Trust Regionscited by 21

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 6; 2024: 6; 2025: 1; 2026: 3.

16 papers.

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  1. 2023Deep Industrial Image Anomaly Detection: A Surveycited by 148
  2. 2024R3D-AD: Reconstruction via Diffusion for 3D Anomaly Detectioncited by 45
  3. 2024PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detectioncited by 30
  4. 2023Target before Shooting: Accurate Anomaly Detection and Localization under One Millisecond via Cascade Patch Retrievalcited by 22
  5. 2024Long-Tailed Anomaly Detection with Learnable Class Namescited by 14
  6. 2024ToCoAD: Two-Stage Contrastive Learning for Industrial Anomaly Detectioncited by 7
  7. 2023Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformerscited by 6
  8. 2024Uni-3DAD: GAN-Inversion Aided Universal 3D Anomaly Detection on Model-free Productscited by 5
  9. 2023Neural Network Training Strategy to Enhance Anomaly Detection Performance: A Perspective on Reconstruction Loss Amplificationcited by 4
  10. 2024ONER: Online Experience Replay for Incremental Anomaly Detectioncited by 3
  11. 2025GenCLIP: Generalizing CLIP Prompts for Zero-shot Anomaly Detectioncited by 3
  12. 2023Two-stream Decoder Feature Normality Estimating Network for Industrial Anomaly Detectioncited by 2
4 more
  1. 2023Excision And Recovery: Visual Defect Obfuscation Based Self-Supervised Anomaly Detection Strategycited by 2
  2. 2026Physics-inspired Pseudo Anomaly Generation and Prototype Feature Guidance for 3D Anomaly Detectioncited by 1
  3. 2026Two Steps Are All You Need: Efficient 3D Point Cloud Anomaly Detection with Consistency Modelscited by 0
  4. 2026Visual Prompting Meets Feature Reconstruction-Based Anomaly Detection with Dual-Teacher Supervisioncited by 0