Method for detecting an anomalous image among a first dataset of images using an adversarial autoencoder

Abstract

A method for detecting an anomalous image among a dataset of images using an Adversarial Autoencoder includes training an Adversarial Autoencoder in a first training with a training dataset of images, with the Adversarial Autoencoder being optimized such that a distribution of latent representations of images of the training dataset of images approaches a predetermined prior distribution and that a reconstruction error of reconstructed images of the training dataset of images is minimized. Subsequently, anomalies are detected in the latent representation and the Adversarial Autoencoder is trained in a second training with the training dataset of images, but taking into account the detected anomalies. The anomalous image among the first dataset of images is detected by the trained Adversarial Autoencoder dependent on at least one of the reconstruction error of the image and a probability density under the predetermined prior distribution.