  We consider the problem of training generative models with deep neural
networks as generators, i.e. to map latent codes to data points. Whereas the
dominant paradigm combines simple priors over codes with complex deterministic
models, we argue that it might be advantageous to use more flexible code
distributions. We demonstrate how these distributions can be induced directly
from the data. The benefits include: more powerful generative models, better
modeling of latent structure and explicit control of the degree of
generalization.
