  It is well known that deep generative models have a rich latent space, and
that it is possible to smoothly manipulate their outputs by traversing this
latent space. Recently, architectures have emerged that allow for more complex
manipulations, such as making an image look as though it were from a different
class, or painted in a certain style. These methods typically require large
amounts of training in order to learn a single class of manipulations. We
present Transflow Learning, a method for transforming a pre-trained generative
model so that its outputs more closely resemble data that we provide
afterwards. In contrast to previous methods, Transflow Learning does not
require any training at all, and instead warps the probability distribution
from which we sample latent vectors using Bayesian inference. Transflow
Learning can be used to solve a wide variety of tasks, such as neural style
transfer and few-shot classification.
