mlsanityData to 7 Oct 2026

Attention Modules Improve Modern Image-Level Anomaly Detection: A DifferNet Case Study

arXiv 2401.08686 ↗First posted 13 Jan 2024cs.CVcited by 0

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Abstract (arXiv)

Within (semi-)automated visual 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 not only alleviate this issue but to furthermore advance the current state of the art in unsupervised visual inspection, this contribution proposes a DifferNet-based solution enhanced with attention modules utilizing SENet and CBAM as backbone - AttentDifferNet - to improve the detection and classification capabilities on three different visual inspection and anomaly detection datasets: MVTec AD, InsPLAD-fault, and Semiconductor Wafer. In comparison to the current state of the art, it is shown that AttentDifferNet achieves improved results, which are, in turn, highlighted throughout our quantitative as well as qualitative evaluation, indicated by a general improvement in AUC of 94.34 vs. 92.46, 96.67 vs. 94.69, and 90.20 vs. 88.74%. 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 anomaly detection.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 27 references that are in the map. First posted on arXiv 13 Jan 2024.

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15 of 27 references.

References · 15

  1. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  2. 2017Squeeze-and-Excitation Networkscited by 4,763
  3. 2018CBAM: Convolutional Block Attention Modulecited by 2,463
  4. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 772
  5. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 591
  6. 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 320
  7. 2020Sub-Image Anomaly Detection with Deep Pyramid Correspondencescited by 297
  8. 2021FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flowscited by 225
  9. 2020Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flowscited by 212
  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
3 more
  1. 2019Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detectioncited by 100
  2. 2020MADGAN: unsupervised Medical Anomaly Detection GAN using multiple adjacent brain MRI slice reconstructioncited by 50
  3. 2022CAINNFlow: Convolutional block Attention modules and Invertible Neural Networks Flow for anomaly detection and localization taskscited by 8

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