  We consider the problem of learning deep generative models from data. We
formulate a method that generates an independent sample via a single
feedforward pass through a multilayer perceptron, as in the recently proposed
generative adversarial networks (Goodfellow et al., 2014). Training a
generative adversarial network, however, requires careful optimization of a
difficult minimax program. Instead, we utilize a technique from statistical
hypothesis testing known as maximum mean discrepancy (MMD), which leads to a
simple objective that can be interpreted as matching all orders of statistics
between a dataset and samples from the model, and can be trained by
backpropagation. We further boost the performance of this approach by combining
our generative network with an auto-encoder network, using MMD to learn to
generate codes that can then be decoded to produce samples. We show that the
combination of these techniques yields excellent generative models compared to
baseline approaches as measured on MNIST and the Toronto Face Database.
