  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.
