  Generative Adversarial Networks are a new family of generative models,
frequently used for generating photorealistic images. The theory promises for
the GAN to eventually reach an equilibrium where generator produces pictures
indistinguishable for the training set. In practice, however, a range of
problems frequently prevents the system from reaching this equilibrium, with
training not progressing ahead due to instabilities or mode collapse. This
paper describes a series of experiments trying to identify patterns in regard
to the effect of the training set on the dynamics and eventual outcome of the
training.
