  Generative networks implicitly approximate complex densities from their
sampling with impressive accuracy. However, because of the enormous scale of
modern datasets, this training process is often computationally expensive. We
cast generative network training into the recent framework of compressive
learning: we reduce the computational burden of large-scale datasets by first
harshly compressing them in a single pass as a single sketch vector. We then
propose a cost function, which approximates the Maximum Mean Discrepancy
metric, but requires only this sketch, which makes it time- and
memory-efficient to optimize.
