  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.
