  We propose a new modeling approach that is a generalization of generative and
discriminative models. The core idea is to use an implicit parameterization of
a joint probability distribution by specifying only the conditional
distributions. The proposed scheme combines the advantages of both worlds -- it
can use powerful complex discriminative models as its parts, having at the same
time better generalization capabilities. We thoroughly evaluate the proposed
method for a simple classification task with artificial data and illustrate its
advantages for real-word scenarios on a semantic image segmentation problem.
