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We study several methods for detecting anomalies in color images, constructedon patch-based auto-encoders. Wecompare the performance of three types ofmethods based, first, on the error between the original image and itsreconstruction,second, on the support estimation of the normal imagedistribution in the latent space, and third, on the error between theoriginalimage and a restored version of the reconstructed image. These methodsare evaluated on the industrial image database MVTecADand compared to twocompetitive state-of-the-art methods.

Anomaly detection in image or latent space of patch-based auto-encoders for industrial image analysis