  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 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.
