mlsanityData to 13 Oct 2026

Attention Modules Improve Image-Level Anomaly Detection for Industrial Inspection: A DifferNet Case Study

arXiv 2311.02747 ↗First posted 5 Nov 2023cs.CVcited by 3

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Abstract (arXiv)

Within (semi-)automated visual industrial inspection, learning-based approaches for assessing visual defects, including deep neural networks, enable the processing of otherwise small defect patterns in pixel size on high-resolution imagery. The emergence of these often rarely occurring defect patterns explains the general need for labeled data corpora. To alleviate this issue and advance the current state of the art in unsupervised visual inspection, this work proposes a DifferNet-based solution enhanced with attention modules: AttentDifferNet. It improves image-level detection and classification capabilities on three visual anomaly detection datasets for industrial inspection: InsPLAD-fault, MVTec AD, and Semiconductor Wafer. In comparison to the state of the art, AttentDifferNet achieves improved results, which are, in turn, highlighted throughout our quali-quantitative study. Our quantitative evaluation shows an average improvement - compared to DifferNet - of 1.77 +/- 0.25 percentage points in overall AUROC considering all three datasets, reaching SOTA results in InsPLAD-fault, an industrial inspection in-the-wild dataset. As our variants to AttentDifferNet show great prospects in the context of currently investigated approaches, a baseline is formulated, emphasizing the importance of attention for industrial anomaly detection both in the wild and in controlled environments.

Key facts. Cited by 3 papers in the map (data through 13 Oct 2026). Builds on 18 of its 48 references that are in the map. First posted on arXiv 5 Nov 2023.

Builds on

18 of 48 references.

References · 18

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  2. 2017Squeeze-and-Excitation Networkscited by 4,772
  3. 2018CBAM: Convolutional Block Attention Modulecited by 2,469
  4. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 774
  5. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 593
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  7. 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 320
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  10. 2023Deep Industrial Image Anomaly Detection: A Surveycited by 147
  11. 2021Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detectioncited by 119
  12. 2021Fully Convolutional Cross-Scale-Flows for Image-based Defect Detectioncited by 117
6 more
  1. 2019Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detectioncited by 100
  2. 2021SA-Net: Shuffle Attention for Deep Convolutional Neural Networkscited by 54
  3. 2020MADGAN: unsupervised Medical Anomaly Detection GAN using multiple adjacent brain MRI slice reconstructioncited by 50
  4. 2023Industrial Anomaly Detection with Domain Shift: A Real-world Dataset and Masked Multi-scale Reconstructioncited by 22
  5. 2023InsPLAD: A Dataset and Benchmark for Power Line Asset Inspection in UAV Imagescited by 15
  6. 2022CAINNFlow: Convolutional block Attention modules and Invertible Neural Networks Flow for anomaly detection and localization taskscited by 8

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

3 papers.

  1. 2024Exploring the Synergies of Hybrid CNNs and ViTs Architectures for Computer Vision: A surveycited by 13
  2. 2025Foundation Models and Transformers for Anomaly Detection: A Surveycited by 8
  3. 2025Unleashing Foundation Vision Models: Adaptive Transfer for Diverse Data-Limited Scientific Domainscited by 2