Abstract: A method for unsupervised learning based anomaly detection of manufactured items, the method may include: obtaining multiple item pixels of an item; determining item features of the item, based on the multiple item pixels and by a non-item specific neural network, the non-item specific neural network is pre-trained to perform feature extraction of objects, at least some of the objects differ from the item; determining, based on the item features, a pixel score for item pixels of the multiple item pixels; for each of the item pixels, calculating a distance between the pixel score and reference pixel-wise distribution information; and for each of the item pixels, determining whether the item pixel is an anomaly pixel based on a comparison between the pixel score and a pixel-wise threshold.

Abstract: There may be provided a method for defect detection, the method may include (a) receiving an image of an evaluated object; (b) applying a distortion removal machine learning process on the image to provide a processed image of the evaluated object; (c) comparing the image to the processed image to provide a comparison result; and (d) detecting one or more object defects based on the comparison result. The distortion removal machine learning process is trained by a training process to remove distortions from images of objects. The training process includes feeding the machine learning process with images of reference objects and with distorted images of the reference objects. The distorted images are generated by distorting the images of the reference.