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
@article{arxiv.1703.06975,
title = {Learning to Generate Samples from Noise through Infusion Training},
author = {Florian Bordes and Sina Honari and Pascal Vincent},
journal= {arXiv preprint arXiv:1703.06975},
year = {2017}
}