Deep Learning for Anomaly Detection in Industrial Vision
In order to detect anomaly in an unsupervised scheme, an autoencoder is trained to reconstruct clean images out of defect-free images corrupted with synthetic noise. During inference an arbitrary (with or without anomaly) image is projected onto the normal space of images. The intensity of the residual map between the original image and its reconstruction estimates the likelihood of a region to be defective.
