Title: Learning to Generate Samples from Noise through Infusion Training,
Abstract: In this work, we investigate a novel training procedure to learn a generative
model as the transition operator of a Markov chain, such that, when applied
repeatedly on an unstructured random noise sample, it will denoise it into a
sample that matches the target distribution from the training set. The novel
training procedure to learn this progressive denoising operation involves
sampling from a slightly different chain than the model chain used for
generation in the absence of a denoising target. In the training chain we
infuse information from the training target example that we would like the
chains to reach with a high probability. The thus learned transition operator
is able to produce quality and varied samples in a small number of steps.
Experiments show competitive results compared to the samples generated with a
basic Generative Adversarial Net