  Semi-supervision in Machine Learning can be used in searches for new physics
where the signal plus background regions are not labelled. This strongly
reduces model dependency in the search for signals Beyond the Standard Model.
This approach displays the drawback in that over-fitting can give rise to fake
signals. Tossing toy Monte Carlo (MC) events can be used to estimate the
corresponding trials factor through a frequentist inference. However, MC events
that are based on full detector simulations are resource intensive. Generative
Adversarial Networks (GANs) can be used to mimic MC generators. GANs are
powerful generative models, but often suffer from training instability. We
henceforth show a review of GANs. We advocate the use of Wasserstein GAN (WGAN)
with weight clipping and WGAN with gradient penalty (WGAN-GP) where the norm of
gradient of the critic is penalized with respect to its input. Following the
emergence of multi-lepton anomalies, we apply GANs for the generation of
di-leptons final states in association with $b$-quarks at the LHC. A good
agreement between the MC and the WGAN-GP generated events is found for the
observables selected in the study.
