mlsanityData to 7 Oct 2026

Self-Supervised Iterative Refinement for Anomaly Detection in Industrial Quality Control

arXiv 2408.11561 ↗First posted 21 Aug 2024cs.CV cs.LGcited by 5

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Abstract (arXiv)

This study introduces the Iterative Refinement Process (IRP), a robust anomaly detection methodology designed for high-stakes industrial quality control. The IRP enhances defect detection accuracy through a cyclic data refinement strategy, iteratively removing misleading data points to improve model performance and robustness. We validate the IRP's effectiveness using two benchmark datasets, Kolektor SDD2 (KSDD2) and MVTec AD, covering a wide range of industrial products and defect types. Our experimental results demonstrate that the IRP consistently outperforms traditional anomaly detection models, particularly in environments with high noise levels. This study highlights the IRP's potential to significantly enhance anomaly detection processes in industrial settings, effectively managing the challenges of sparse and noisy data.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 14 of its 29 references that are in the map. First posted on arXiv 21 Aug 2024.

Builds on

14 of 29 references.

References · 14

  1. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 772
  2. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 591
  3. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 510
  4. 2018GANomaly: Semi-Supervised Anomaly Detection via Adversarial Trainingcited by 409
  5. 2020Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flowscited by 212
  6. 2021Mixed supervision for surface-defect detection: from weakly to fully supervised learningcited by 101
  7. 2022DSR -- A dual subspace re-projection network for surface anomaly detectioncited by 89
  8. 2019Robust Anomaly Detection in Images using Adversarial Autoencoderscited by 38
  9. 2017Anomaly Detection by Robust Statisticscited by 22
  10. 2020End-to-end training of a two-stage neural network for defect detectioncited by 12
  11. 2024Diffusion-based Image Generation for In-distribution Data Augmentation in Surface Defect Detectioncited by 9
  12. 2024Leveraging Latent Diffusion Models for Training-Free In-Distribution Data Augmentation for Surface Defect Detectioncited by 8
2 more
  1. 2019RAD: On-line Anomaly Detection for Highly Unreliable Datacited by 3
  2. 2022Robust Anomaly Detection for Time-series Datacited by 2

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Cited by year

Citing papers per year: 2025: 5.

5 papers.

  1. 2025Towards Real Unsupervised Anomaly Detection Via Confident Meta-Learningcited by 8
  2. 2025Meta Learning-Driven Iterative Refinement for Robust Anomaly Detection in Industrial Inspectioncited by 5
  3. 2025ExDD: Explicit Dual Distribution Learning for Surface Defect Detection via Diffusion Synthesiscited by 0
  4. 2025RoadFusion: Latent Diffusion Model for Pavement Defect Detectioncited by 0
  5. 2025Robust Anomaly Detection in Industrial Environments via Meta-Learningcited by 0