  Identifying defects in the images of industrial products has been an
important task to enhance quality control and reduce maintenance costs. In
recent studies, industrial anomaly detection models were developed using
pre-trained networks to learn nominal representations. To employ the relative
positional information of each pixel, we present \textit{\textbf{N-pad}}, a
novel method for anomaly detection and segmentation in a one-class learning
setting that includes the neighborhood of the target pixel for model training
and evaluation. Within the model architecture, pixel-wise nominal distributions
are estimated by using the features of neighboring pixels with the target pixel
to allow possible marginal misalignment. Moreover, the centroids from clusters
of nominal features are identified as a representative nominal set.
Accordingly, anomaly scores are inferred based on the Mahalanobis distances and
Euclidean distances between the target pixel and the estimated distributions or
the centroid set, respectively. Thus, we have achieved state-of-the-art
performance in MVTec-AD with AUROC of 99.37 for anomaly detection and 98.75 for
anomaly segmentation, reducing the error by 34\% compared to the next best
performing model. Experiments in various settings further validate our model.
