  Anomaly detection is a difficult problem in many areas and has recently been
subject to a lot of attention. Classifying unseen data as anomalous is a
challenging matter. Latest proposed methods rely on Generative Adversarial
Networks (GANs) to estimate the normal data distribution, and produce an
anomaly score prediction for any given data. In this article, we propose a
simple yet new adversarial method to tackle this problem, denoted as
History-based anomaly detector (HistoryAD). It consists of a self-supervised
model, trained to recognize 'normal' samples by comparing them to samples based
on the training history of a previously trained GAN. Quantitative and
qualitative results are presented evaluating its performance. We also present a
comparison to several state-of-the-art methods for anomaly detection showing
that our proposal achieves top-tier results on several datasets.
