  A new form of the variational autoencoder (VAE) is proposed, based on the
symmetric Kullback-Leibler divergence. It is demonstrated that learning of the
resulting symmetric VAE (sVAE) has close connections to previously developed
adversarial-learning methods. This relationship helps unify the previously
distinct techniques of VAE and adversarially learning, and provides insights
that allow us to ameliorate shortcomings with some previously developed
adversarial methods. In addition to an analysis that motivates and explains the
sVAE, an extensive set of experiments validate the utility of the approach.
