  In industrial manufacturing processes, errors frequently occur at
unpredictable times and in unknown manifestations. We tackle the problem of
automatic defect detection without requiring any image samples of defective
parts. Recent works model the distribution of defect-free image data, using
either strong statistical priors or overly simplified data representations. In
contrast, our approach handles fine-grained representations incorporating the
global and local image context while flexibly estimating the density. To this
end, we propose a novel fully convolutional cross-scale normalizing flow
(CS-Flow) that jointly processes multiple feature maps of different scales.
Using normalizing flows to assign meaningful likelihoods to input samples
allows for efficient defect detection on image-level. Moreover, due to the
preserved spatial arrangement the latent space of the normalizing flow is
interpretable which enables to localize defective regions in the image. Our
work sets a new state-of-the-art in image-level defect detection on the
benchmark datasets Magnetic Tile Defects and MVTec AD showing a 100% AUROC on 4
out of 15 classes.
