mlsanityData to 14 Oct 2026

A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images

arXiv 2204.11161 ↗First posted 24 Apr 2022cs.CVcited by 21

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Abstract (arXiv)

In line with the development of Industry 4.0, surface defect detection/anomaly detection becomes a topical subject in the industry field. Improving efficiency as well as saving labor costs has steadily become a matter of great concern in practice, where deep learning-based algorithms perform better than traditional vision inspection methods in recent years. While existing deep learning-based algorithms are biased towards supervised learning, which not only necessitates a huge amount of labeled data and human labor, but also brings about inefficiency and limitations. In contrast, recent research shows that unsupervised learning has great potential in tackling the above disadvantages for visual industrial anomaly detection. In this survey, we summarize current challenges and provide a thorough overview of recently proposed unsupervised algorithms for visual industrial anomaly detection covering five categories, whose innovation points and frameworks are described in detail. Meanwhile, publicly available datasets for industrial anomaly detection are introduced. By comparing different classes of methods, the advantages and disadvantages of anomaly detection algorithms are summarized. Based on the current research framework, we point out the core issue that remains to be resolved and provide further improvement directions. Meanwhile, based on the latest technological trends, we offer insights into future research directions. It is expected to assist both the research community and industry in developing a broader and cross-domain perspective.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). Builds on 61 of its 170 references that are in the map. First posted on arXiv 24 Apr 2022.

Builds on

61 of 170 references.

References · 61

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38 more
  1. 2020CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instancescited by 441
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 9; 2026: 9.

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  1. 2025Foundation Models and Transformers for Anomaly Detection: A Surveycited by 8
  2. 2026Failure Identification in Imitation Learning Via Statistical and Semantic Filteringcited by 6
  3. 2025Building Machine Learning Challenges for Anomaly Detection in Sciencecited by 5
  4. 2025AnoStyler: Text-Driven Localized Anomaly Generation via Lightweight Style Transfercited by 5
  5. 2025VLMDiff: Leveraging Vision-Language Models for Multi-Class Anomaly Detection with Diffusioncited by 3
  6. 2025ISP-AD: A Large-Scale Real-World Dataset for Advancing Industrial Anomaly Detection with Synthetic and Real Defectscited by 2
  7. 2024Benchmarking Anomaly Detection Algorithms: Deep Learning and Beyondcited by 1
  8. 2024Domain-independent detection of known anomaliescited by 1
  9. 2025Beyond Academic Benchmarks: Critical Analysis and Best Practices for Visual Industrial Anomaly Detectioncited by 1
  10. 2025Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathologycited by 1
  11. 2025AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domaincited by 1
  12. 2024Unsupervised Anomaly Detection Using Diffusion Trend Analysis for Display Inspectioncited by 0
9 more
  1. 2025Automated Annotation of Shearographic Measurements Enabling Weakly Supervised Defect Detectioncited by 0
  2. 2026Drift-Aware Variational Autoencoder-based Anomaly Detection with Two-level Ensemblingcited by 0
  3. 2026Native Extrapolation Awareness in Flow-Based Conditional Generationcited by 0
  4. 2026BAAF: Universal Transformation of One-Class Classifiers for Unsupervised Image Anomaly Detectioncited by 0
  5. 2026GATE-AD: Graph Attention Network Encoding For Few-Shot Industrial Visual Anomaly Detectioncited by 0
  6. 2026Uni-RCM: Unified Reference-guided Cross-modal Mapping for Multi-Class Anomaly Detectioncited by 0
  7. 2026Plan First, Judge Later, Run Better: A DMAIC-Inspired Agentic System for Industrial Anomaly Detectioncited by 0
  8. 2026Unsupervised Anomaly Detection for Image Dataset Quality Assurance in Multi-Center Breast MRIcited by 0
  9. 2026TED:Text-Axis Evidence Decomposition for Prompted Anomaly Localizationcited by 0