mlsanityData to 7 Oct 2026

Diversity-Measurable Anomaly Detection

arXiv 2303.05047 ↗First posted 9 Mar 2023cs.CV cs.AIcited by 41

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Abstract (arXiv)

Reconstruction-based anomaly detection models achieve their purpose by suppressing the generalization ability for anomaly. However, diverse normal patterns are consequently not well reconstructed as well. Although some efforts have been made to alleviate this problem by modeling sample diversity, they suffer from shortcut learning due to undesired transmission of abnormal information. In this paper, to better handle the tradeoff problem, we propose Diversity-Measurable Anomaly Detection (DMAD) framework to enhance reconstruction diversity while avoid the undesired generalization on anomalies. To this end, we design Pyramid Deformation Module (PDM), which models diverse normals and measures the severity of anomaly by estimating multi-scale deformation fields from reconstructed reference to original input. Integrated with an information compression module, PDM essentially decouples deformation from prototypical embedding and makes the final anomaly score more reliable. Experimental results on both surveillance videos and industrial images demonstrate the effectiveness of our method. In addition, DMAD works equally well in front of contaminated data and anomaly-like normal samples.

Key facts. Cited by 41 papers in the map (data through 7 Oct 2026). Builds on 30 of its 42 references that are in the map. First posted on arXiv 9 Mar 2023.

Builds on

30 of 42 references.

References · 30

  1. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
  2. 2017Neural Discrete Representation Learningcited by 4,650
  3. 2017Decoupled Weight Decay Regularizationcited by 1,722
  4. 2017Deformable Convolutional Networkscited by 1,481
  5. 2015Spatial Transformer Networkscited by 1,423
  6. 2017Cascade R-CNN: Delving into High Quality Object Detectioncited by 1,275
  7. 2016FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networkscited by 780
  8. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 772
  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. 2019Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detectioncited by 560
  12. 2021CutPaste: Self-Supervised Learning for Anomaly Detection and Localizationcited by 529
18 more
  1. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 510
  2. 2018Do Deep Generative Models Know What They Don't Know?cited by 443
  3. 2022Anomaly Detection via Reverse Distillation from One-Class Embeddingcited by 419
  4. 2018GANomaly: Semi-Supervised Anomaly Detection via Adversarial Trainingcited by 409
  5. 2017Future Frame Prediction for Anomaly Detection -- A New Baselinecited by 391
  6. 2018An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solutioncited by 302
  7. 2020Learning Memory-guided Normality for Anomaly Detectioncited by 278
  8. 2016Learning Temporal Regularity in Video Sequencescited by 254
  9. 2020Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentationcited by 212
  10. 2020Anomaly Detection in Video via Self-Supervised and Multi-Task Learningcited by 147
  11. 2017Convolutional neural network architecture for geometric matchingcited by 138
  12. 2022Registration based Few-Shot Anomaly Detectioncited by 133
  13. 2021A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Predictioncited by 129
  14. 2018Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Videocited by 110
  15. 2021Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detectioncited by 83
  16. 2021Learning Normal Dynamics in Videos with Meta Prototype Networkcited by 71
  17. 2021Locally Aware Piecewise Transformation Fields for 3D Human Mesh Registrationcited by 41
  18. 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 7 Oct)

Citing papers per year: 2023: 7; 2024: 16; 2025: 13; 2026: 5.

41 papers.

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  1. 2024Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Promptscited by 88
  2. 2023Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detectioncited by 47
  3. 2023DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detectioncited by 46
  4. 2025AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detectioncited by 28
  5. 2024Deep Learning for Video Anomaly Detection: A Reviewcited by 27
  6. 2024MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objectscited by 26
  7. 2023Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detectioncited by 25
  8. 2023Target before Shooting: Accurate Anomaly Detection and Localization under One Millisecond via Cascade Patch Retrievalcited by 22
  9. 2024Open-World Semantic Segmentation Including Class Similaritycited by 22
  10. 2024Fine-grained Abnormality Prompt Learning for Zero-shot Anomaly Detectioncited by 22
  11. 2025Learning to Detect Multi-class Anomalies with Just One Normal Image Promptcited by 18
  12. 2024Learning Feature Inversion for Multi-class Anomaly Detection under General-purpose COCO-AD Benchmarkcited by 15
29 more
  1. 2024Salvaging the Overlooked: Leveraging Class-Aware Contrastive Learning for Multi-Class Anomaly Detectioncited by 15
  2. 2025Handling Out-of-Distribution Data: A Surveycited by 14
  3. 2024Rethinking Reconstruction-based Graph-Level Anomaly Detection: Limitations and a Simple Remedycited by 13
  4. 2025MetaUAS: Universal Anomaly Segmentation with One-Prompt Meta-Learningcited by 12
  5. 2024Context-aware Video Anomaly Detection in Long-Term Datasetscited by 11
  6. 2025Normal-Abnormal Guided Generalist Anomaly Detectioncited by 10
  7. 2024VideoPatchCore: An Effective Method to Memorize Normality for Video Anomaly Detectioncited by 8
  8. 2025Anomaly Detection and Generation with Diffusion Models: A Surveycited by 8
  9. 2024Toward Multi-class Anomaly Detection: Exploring Class-aware Unified Model against Inter-class Interferencecited by 7
  10. 2025Registration is a Powerful Rotation-Invariance Learner for 3D Anomaly Detectioncited by 7
  11. 2023Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformerscited by 6
  12. 2024A Novel Approach to Industrial Defect Generation through Blended Latent Diffusion Model with Online Adaptationcited by 6
  13. 2024Vision-Language Models Assisted Unsupervised Video Anomaly Detectioncited by 6
  14. 2025The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLMcited by 4
  15. 2024IPAD: Industrial Process Anomaly Detection Datasetcited by 3
  16. 2025Detecting Contextual Anomalies by Discovering Consistent Spatial Regionscited by 3
  17. 2025From Objects to Events: Unlocking Complex Visual Understanding in Object Detectors via LLM-guided Symbolic Reasoningcited by 3
  18. 2025A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospectscited by 2
  19. 2025VADER: Towards Causal Video Anomaly Understanding with Relation-Aware Large Language Modelscited by 2
  20. 2025ADNet: A Large-Scale and Extensible Multi-Domain Benchmark for Anomaly Detection Across 380 Real-World Categoriescited by 2
  21. 2026RAID: Retrieval-Augmented Anomaly Detectioncited by 2
  22. 2023Divide and Conquer in Video Anomaly Detection: A Comprehensive Review and New Approachcited by 1
  23. 2023Human Kinematics-inspired Skeleton-based Video Anomaly Detectioncited by 1
  24. 2024Reconstruction-Based Anomaly Localization via Knowledge-Informed Self-Trainingcited by 1
  25. 2024Identifying Spatio-Temporal Drivers of Extreme Eventscited by 1
  26. 2026BAAF: Universal Transformation of One-Class Classifiers for Unsupervised Image Anomaly Detectioncited by 0
  27. 2026Component-Based Out-of-Distribution Detectioncited by 0
  28. 2026Learning to Identify Out-of-Distribution Objects for 3D LiDAR Anomaly Segmentationcited by 0
  29. 2026Beyond Normal References: Discriminative Few-Shot Anomaly Detectioncited by 0