  Maximum mean discrepancy (MMD) has been successfully applied to learn deep
generative models for characterizing a joint distribution of variables via
kernel mean embedding. In this paper, we present conditional generative moment-
matching networks (CGMMN), which learn a conditional distribution given some
input variables based on a conditional maximum mean discrepancy (CMMD)
criterion. The learning is performed by stochastic gradient descent with the
gradient calculated by back-propagation. We evaluate CGMMN on a wide range of
tasks, including predictive modeling, contextual generation, and Bayesian dark
knowledge, which distills knowledge from a Bayesian model by learning a
relatively small CGMMN student network. Our results demonstrate competitive
performance in all the tasks.
