  We present a compression algorithm for parton densities using synthetic
replicas generated from the training of a Generative Adversarial Network (GAN).
The generated replicas are used to further enhance the statistics of a given
Monte Carlo PDF set prior to compression. This results in a compression
methodology that is able to provide a compressed set with smaller number of
replicas and a more adequate representation of the original probability
distribution. We also address the question of whether the GAN could be used as
an alternative mechanism to avoid the fitting of large number of replicas.
