  We present a novel adversarial framework for training deep belief networks
(DBNs), which includes replacing the generator network in the methodology of
generative adversarial networks (GANs) with a DBN and developing a highly
parallelizable numerical algorithm for training the resulting architecture in a
stochastic manner. Unlike the existing techniques, this framework can be
applied to the most general form of DBNs with no requirement for back
propagation. As such, it lays a new foundation for developing DBNs on a par
with GANs with various regularization units, such as pooling and normalization.
Foregoing back-propagation, our framework also exhibits superior scalability as
compared to other DBN and GAN learning techniques. We present a number of
numerical experiments in computer vision as well as neurosciences to illustrate
the main advantages of our approach.

Machine Learning;Machine Learning