  We introduce the "Energy-based Generative Adversarial Network" model (EBGAN)
which views the discriminator as an energy function that attributes low
energies to the regions near the data manifold and higher energies to other
regions. Similar to the probabilistic GANs, a generator is seen as being
trained to produce contrastive samples with minimal energies, while the
discriminator is trained to assign high energies to these generated samples.
Viewing the discriminator as an energy function allows to use a wide variety of
architectures and loss functionals in addition to the usual binary classifier
with logistic output. Among them, we show one instantiation of EBGAN framework
as using an auto-encoder architecture, with the energy being the reconstruction
error, in place of the discriminator. We show that this form of EBGAN exhibits
more stable behavior than regular GANs during training. We also show that a
single-scale architecture can be trained to generate high-resolution images.
