  Detecting surface anomalies of industrial materials poses a significant
challenge within a myriad of industrial manufacturing processes. In recent
times, various methodologies have emerged, capitalizing on the advantages of
employing a network pre-trained on natural images for the extraction of
representative features. Subsequently, these features are subjected to
processing through a diverse range of techniques including memory banks,
normalizing flow, and knowledge distillation, which have exhibited exceptional
accuracy. This paper revisits approaches based on pre-trained features by
introducing a novel method centered on target-specific embedding. To capture
the most representative features of the texture under consideration, we employ
a variant of a contrastive training procedure that incorporates both
artificially generated defective samples and anomaly-free samples during
training. Exploiting the intrinsic properties of surfaces, we derived a
meaningful representation from the defect-free samples during training,
facilitating a straightforward yet effective calculation of anomaly scores. The
experiments conducted on the MVTEC AD and TILDA datasets demonstrate the
competitiveness of our approach compared to state-of-the-art methods.
