mlsanityData to 14 Oct 2026

Composite Score for Anomaly Detection in Imbalanced Real-World Industrial Dataset

arXiv 2211.15513 ↗First posted 25 Nov 2022cs.CV cs.LGcited by 7

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Abstract (arXiv)

In recent years, the industrial sector has evolved towards its fourth revolution. The quality control domain is particularly interested in advanced machine learning for computer vision anomaly detection. Nevertheless, several challenges have to be faced, including imbalanced datasets, the image complexity, and the zero-false-negative (ZFN) constraint to guarantee the high-quality requirement. This paper illustrates a use case for an industrial partner, where Printed Circuit Board Assembly (PCBA) images are first reconstructed with a Vector Quantized Generative Adversarial Network (VQGAN) trained on normal products. Then, several multi-level metrics are extracted on a few normal and abnormal images, highlighting anomalies through reconstruction differences. Finally, a classifer is trained to build a composite anomaly score thanks to the metrics extracted. This three-step approach is performed on the public MVTec-AD datasets and on the partner PCBA dataset, where it achieves a regular accuracy of 95.69% and 87.93% under the ZFN constraint.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 12 of its 39 references that are in the map. First posted on arXiv 25 Nov 2022.

Builds on

12 of 39 references.

References · 12

  1. 2017Neural Discrete Representation Learningcited by 4,712
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  10. 2018An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videoscited by 45
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  12. 2021Constrained Optimization to Train Neural Networks on Critical and Under-Represented Classescited by 19

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 2; 2025: 1; 2026: 2.

7 papers.

  1. 2024Incomplete Multimodal Industrial Anomaly Detection via Cross-Modal Distillationcited by 6
  2. 2023Reconstruction Error-based Anomaly Detection with Few Outlying Examplescited by 5
  3. 2024AssemAI: Interpretable Image-Based Anomaly Detection for Manufacturing Pipelinescited by 3
  4. 2026Synthetic Augmentation in Imbalanced Learning: When It Helps, When It Hurts, and How Much to Addcited by 3
  5. 2023Industrial and Medical Anomaly Detection Through Cycle-Consistent Adversarial Networkscited by 2
  6. 2026Cross-Modal Mapping and Dual-Branch Reconstruction for 2D-3D Multimodal Industrial Anomaly Detectioncited by 1
  7. 2025Towards a Trustworthy Anomaly Detection for Critical Applications through Approximated Partial AUC Losscited by 0

Similar, not cited

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  1. 2025LR-IAD:Mask-Free Industrial Anomaly Detection with Logical Reasoning
  2. 2024Unified Anomaly Detection methods on Edge Device using Knowledge Distillation and Quantization
  3. 2025Enhancing Glass Defect Detection with Diffusion Models: Addressing Imbalanced Datasets in Manufacturing Quality Control
  4. 2022Unsupervised Visual Defect Detection with Score-Based Generative Model
  5. 2024A Novel Approach to Industrial Defect Generation through Blended Latent Diffusion Model with Online Adaptation
  6. 2025Exploring "Many in Few" and "Few in Many" Properties in Long-Tailed, Highly-Imbalanced IC Defect Classification
  7. 2025Rebalancing the Scales: A Systematic Mapping Study of Generative Adversarial Networks (GANs) in Addressing Data Imbalance
  8. 2025Noise Fusion-based Distillation Learning for Anomaly Detection in Complex Industrial Environments