Autoencoder networks, trained only on QCD jets, can be used to search for
anomalies in jet-substructure. We show how, based either on images or on
4-vectors, they identify jets from decays of arbitrary heavy resonances. To
control the backgrounds and the underlying systematics we can de-correlate the
jet mass using an adversarial network. Such an adversarial autoencoder allows
for a general and at the same time easily controllable search for new physics.
Ideally, it can be trained and applied to data in the same phase space region,
allowing us to efficiently search for new physics using un-supervised learning.