Abstract
Anomaly detection is used to identify abnormal observations that don't follow a normal pattern. In
this work, we use the power of Generative Adversarial Networks in sampling from image distributions
to perform anomaly detection with images and to identify local anomalous segments within this
images. Also, we explore potential application of this method to support pathological analysis of
biological tissues.
