Papers › Attention Modules Improve Image-Level Anomaly Detection for Industrial Inspection: A...
Attention Modules Improve Image-Level Anomaly Detection for Industrial Inspection: A DifferNet Case Study
André Luiz Buarque Vieira e Silva, Francisco Simões, Danny Kowerko, Tobias Schlosser, Felipe Battisti, Veronica Teichrieb
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.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Anomaly Detection | InsPLAD | AttentDifferNet (SENet-AlexNet) | Detection AUROC | 94.34 | #1 of 5 | Archive leaderboard | report |
| Anomaly Detection | InsPLAD | RD++ (CBAM-ResNet-18) | Detection AUROC | 90.75 | #3 of 5 | Archive leaderboard | report |
| Anomaly Detection | InsPLAD | RD++ (SENet-ResNet-18) | Detection AUROC | 90.32 | #4 of 5 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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