Data to 7 Oct 2026

Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentation

arXiv 2006.16067 ↗First posted 29 Jun 2020cs.CVcited by 212

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Abstract (arXiv)

In this paper, we address the problem of image anomaly detection and segmentation. Anomaly detection involves making a binary decision as to whether an input image contains an anomaly, and anomaly segmentation aims to locate the anomaly on the pixel level. Support vector data description (SVDD) is a long-standing algorithm used for an anomaly detection, and we extend its deep learning variant to the patch-based method using self-supervised learning. This extension enables anomaly segmentation and improves detection performance. As a result, anomaly detection and segmentation performances measured in AUROC on MVTec AD dataset increased by 9.8% and 7.0%, respectively, compared to the previous state-of-the-art methods. Our results indicate the efficacy of the proposed method and its potential for industrial application. Detailed analysis of the proposed method offers insights regarding its behavior, and the code is available online.

Key facts. Cited by 212 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Jun 2020.

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Citing papers per year: 2021: 30; 2022: 39; 2023: 52; 2024: 45; 2025: 27; 2026: 19.

212 papers.

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2021Towards Total Recall in Industrial Anomaly Detectioncited by 772
2021CutPaste: Self-Supervised Learning for Anomaly Detection and Localizationcited by 529
2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 510
2022Anomaly Detection via Reverse Distillation from One-Class Embeddingcited by 419
2022SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentationcited by 397
2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 320
2023WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentationcited by 311
2022A Unified Model for Multi-class Anomaly Detectioncited by 267
2021FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flowscited by 225
2023AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Modelscited by 169
2022DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detectioncited by 153
2023Deep Industrial Image Anomaly Detection: A Surveycited by 147

38 more

2021Student-Teacher Feature Pyramid Matching for Anomaly Detectioncited by 145
2022CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly Localizationcited by 135
2022Registration based Few-Shot Anomaly Detectioncited by 133
2021Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detectioncited by 119
2021Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localizationcited by 116
2024RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detectioncited by 116
2022Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detectioncited by 104
2024PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detectioncited by 97
2021Towards a Rigorous Evaluation of Time-series Anomaly Detectioncited by 95
2021Inpainting Transformer for Anomaly Detectioncited by 91
2024Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Promptscited by 88
2023BMAD: Benchmarks for Medical Anomaly Detectioncited by 81
2022Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detectioncited by 79
2023PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flowcited by 74
2022Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detectioncited by 72
2021Explainable Deep Few-shot Anomaly Detection with Deviation Networkscited by 69
2022PNI : Industrial Anomaly Detection using Position and Neighborhood Informationcited by 69
2022MemSeg: A semi-supervised method for image surface defect detection using differences and commonalitiescited by 68
2022Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Surveycited by 67
2023Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detectioncited by 66
2023IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturingcited by 64
2023ReContrast: Domain-Specific Anomaly Detection via Contrastive Reconstructioncited by 62
2021A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly Detectioncited by 60
2023Segment Any Anomaly without Training via Hybrid Prompt Regularizationcited by 59
2021OpenFL: An open-source framework for Federated Learningcited by 58
2022Prototypical Residual Networks for Anomaly Detection and Localizationcited by 56
2023Collaborative Discrepancy Optimization for Reliable Image Anomaly Localizationcited by 56
2024SoftPatch: Unsupervised Anomaly Detection with Noisy Datacited by 55
2024AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2cited by 53
2024MuSc: Zero-Shot Industrial Anomaly Classification and Segmentation with Mutual Scoring of the Unlabeled Imagescited by 52
2023PAD: A Dataset and Benchmark for Pose-agnostic Anomaly Detectioncited by 46
2021Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Imagescited by 45
2023Diversity-Measurable Anomaly Detectioncited by 41
2023Multimodal Industrial Anomaly Detection by Crossmodal Feature Mappingcited by 41
2024Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Surveycited by 36
2023Anomaly Detection under Distribution Shiftcited by 35
2024Do LLMs Understand Visual Anomalies? Uncovering LLM's Capabilities in Zero-shot Anomaly Detectioncited by 35
2024MediCLIP: Adapting CLIP for Few-shot Medical Image Anomaly Detectioncited by 35

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