mlsanityData to 7 Oct 2026

Anomaly Detection via Self-organizing Map

arXiv 2107.09903 ↗First posted 21 Jul 2021cs.CVcited by 18

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Abstract (arXiv)

Anomaly detection plays a key role in industrial manufacturing for product quality control. Traditional methods for anomaly detection are rule-based with limited generalization ability. Recent methods based on supervised deep learning are more powerful but require large-scale annotated datasets for training. In practice, abnormal products are rare thus it is very difficult to train a deep model in a fully supervised way. In this paper, we propose a novel unsupervised anomaly detection approach based on Self-organizing Map (SOM). Our method, Self-organizing Map for Anomaly Detection (SOMAD) maintains normal characteristics by using topological memory based on multi-scale features. SOMAD achieves state-of the-art performance on unsupervised anomaly detection and localization on the MVTec dataset.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 19 of its 29 references that are in the map. First posted on arXiv 21 Jul 2021.

Builds on

19 of 29 references.

References · 19

  1. 2013Auto-Encoding Variational Bayescited by 3,980
  2. 2016Wide Residual Networkscited by 3,057
  3. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 591
  4. 2019Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detectioncited by 560
  5. 2018GANomaly: Semi-Supervised Anomaly Detection via Adversarial Trainingcited by 409
  6. 2019Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddingscited by 386
  7. 2020Sub-Image Anomaly Detection with Deep Pyramid Correspondencescited by 297
  8. 2020Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentationcited by 212
  9. 2020Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flowscited by 212
  10. 2019OCGAN: One-class Novelty Detection Using GANs with Constrained Latent Representationscited by 199
  11. 2020Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly Detectioncited by 115
  12. 2019Towards Visually Explaining Variational Autoencoderscited by 113
7 more
  1. 2020Deep Nearest Neighbor Anomaly Detectioncited by 103
  2. 2018Generative Probabilistic Novelty Detection with Adversarial Autoencoderscited by 99
  3. 2020Anomaly Detection With Conditional Variational Autoencoderscited by 46
  4. 2015Loss Functions for Neural Networks for Image Processingcited by 41
  5. 2020Anomaly localization by modeling perceptual featurescited by 21
  6. 2020Novelty Detection Through Model-Based Characterization of Neural Networkscited by 17
  7. 2020Image Anomaly Detection by Aggregating Deep Pyramidal Representationscited by 4

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Citing papers per year: 2021: 1; 2022: 4; 2023: 7; 2024: 4; 2025: 2.

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  1. 2023Deep Industrial Image Anomaly Detection: A Surveycited by 147
  2. 2023BMAD: Benchmarks for Medical Anomaly Detectioncited by 81
  3. 2022PNI : Industrial Anomaly Detection using Position and Neighborhood Informationcited by 69
  4. 2022Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Surveycited by 67
  5. 2023IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturingcited by 64
  6. 2023Label-Free Liver Tumor Segmentationcited by 56
  7. 2022A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Imagescited by 21
  8. 2023LafitE: Latent Diffusion Model with Feature Editing for Unsupervised Multi-class Anomaly Detectioncited by 13
  9. 2023What makes a good data augmentation for few-shot unsupervised image anomaly detection?cited by 8
  10. 2021Efficient Anomaly Detection Using Self-Supervised Multi-Cue Taskscited by 6
  11. 2024Few-shot Online Anomaly Detection and Segmentationcited by 5
  12. 2024A Comprehensive Survey on Machine Learning Driven Material Defect Detectioncited by 5
6 more
  1. 2022Self-Supervised Training with Autoencoders for Visual Anomaly Detectioncited by 3
  2. 2023Convolutional autoencoder-based multimodal one-class classificationcited by 1
  3. 2024Domain-independent detection of known anomaliescited by 1
  4. 2024Hybrid Unsupervised Learning Strategy for Monitoring Industrial Batch Processescited by 0
  5. 2025A Review of Artificial Intelligence Impacting Statistical Process Monitoring and Future Directionscited by 0
  6. 2025High Dimensional Data Decomposition for Anomaly Detection of Textured Imagescited by 0