  We propose a framework for generating samples from a probability distribution
that differs from the probability distribution of the training set. We use an
adversarial process that simultaneously trains three networks, a generator and
two discriminators. We refer to this new model as regularized generative
adversarial network (RegGAN). We evaluate RegGAN on a synthetic dataset
composed of gray scale images and we further show that it can be used to learn
some pre-specified notions in topology (basic topology properties). The work is
motivated by practical problems encountered while using generative methods in
the art world.
