  Machine learning approaches now achieve impressive generation capabilities in
numerous domains such as image, audio or video. However, most training \&
evaluation frameworks revolve around the idea of strictly modelling the
original data distribution rather than trying to extrapolate from it. This
precludes the ability of such models to diverge from the original distribution
and, hence, exhibit some creative traits. In this paper, we propose various
perspectives on how this complicated goal could ever be achieved, and provide
preliminary results on our novel training objective called \textit{Bounded
Adversarial Divergence} (BAD).
