  The application of deep learning in visual anomaly detection has gained
widespread popularity due to its potential use in quality control and
manufacturing. Current standard methods are Unsupervised, where a clean dataset
is utilised to detect deviations and flag anomalies during testing. However,
incorporating a few samples when the type of anomalies is known beforehand can
significantly enhance performance. Thus, we propose ATAC-Net, a framework that
trains to detect anomalies from a minimal set of known prior anomalies.
Furthermore, we introduce attention-guided cropping, which provides a closer
view of suspect regions during the training phase. Our framework is a reliable
and easy-to-understand system for detecting anomalies, and we substantiate its
superiority to some of the current state-of-the-art techniques in a comparable
setting.
