  Probabilistic models are often trained by maximum likelihood, which
corresponds to minimizing a specific f-divergence between the model and data
distribution. In light of recent successes in training Generative Adversarial
Networks, alternative non-likelihood training criteria have been proposed.
Whilst not necessarily statistically efficient, these alternatives may better
match user requirements such as sharp image generation. A general variational
method for training probabilistic latent variable models using maximum
likelihood is well established; however, how to train latent variable models
using other f-divergences is comparatively unknown. We discuss a variational
approach that, when combined with the recently introduced Spread Divergence,
can be applied to train a large class of latent variable models using any
f-divergence.
