  The detection of phase transitions in quantum many-body systems with lowest
possible prior knowledge of their details is among the most rousing goals of
the flourishing application of machine-learning techniques to physical
questions. Here, we train a Generative Adversarial Network (GAN) with the
Entanglement Spectrum of a system bipartition, as extracted by means of Matrix
Product States ans\"atze. We are able to identify gapless-to-gapped phase
transitions in different one-dimensional models by looking at the machine
inability to reconstruct outsider data with respect to the training set. We
foresee that GAN-based methods will become instrumental in anomaly detection
schemes applied to the determination of phase-diagrams.
