Anomaly detection in image or latent space of patch-based auto-encoders
  for industrial image analysis
Nicolas Pinon Robin Trombetta Carole Lartizien
We study several methods for detecting anomalies in color images, constructed
on patch-based auto-encoders. Wecompare the performance of three types of
methods based, first, on the error between the original image and its
reconstruction,second, on the support estimation of the normal image
distribution in the latent space, and third, on the error between the
originalimage and a restored version of the reconstructed image. These methods
are evaluated on the industrial image database MVTecADand compared to two
competitive state-of-the-art methods.