  The idea of adversarial learning of regularization functionals has recently
been introduced in the wider context of inverse problems. The intuition behind
this method is the realization that it is not only necessary to learn the basic
features that make up a class of signals one wants to represent, but also, or
even more so, which features to avoid in the representation. In this paper, we
will apply this approach to the training of generative models, leading to what
we call Maximum Discrepancy Generative Regularization. In particular, we apply
this to problem of source separation by means of Non-negative Matrix
Factorization (NMF) and present a new method for the adversarial training of
NMF bases. We show in numerical experiments, both for image and audio
separation, that this leads to a clear improvement of the reconstructed
signals, in particular in the case where little or no strong supervision data
is available.
