Open Peer Review. Open Publishing. Open Access. Open Discussion. Open Directory. Open Recommendations. Open API. Open Source.
Learning to Generate Samples from Noise through Infusion Training
Florian Bordes, Sina Honari, Pascal Vincent
Nov 05, 2016 (modified: Mar 03, 2017)ICLR 2017 conference submissionreaders: everyone
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.
TL;DR:We learn a markov transition operator acting on inputspace, to denoise random noise into a target distribution. We use a novel target injection technique to guide the training.
Keywords:Deep learning, Unsupervised Learning
Enter your feedback below and we'll get back to you as soon as possible.