{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/learning-to-generate-samples-from-noise","title":"Learning to Generate Samples from Noise through Infusion Training","arxiv_id":"1703.06975","date":"2017-03-20","proceeding":null,"authors":["Florian Bordes","Sina Honari","Pascal Vincent"],"abstract":"In this work, we investigate a novel training procedure to learn a generative\nmodel as the transition operator of a Markov chain, such that, when applied\nrepeatedly on an unstructured random noise sample, it will denoise it into a\nsample that matches the target distribution from the training set. The novel\ntraining procedure to learn this progressive denoising operation involves\nsampling from a slightly different chain than the model chain used for\ngeneration in the absence of a denoising target. In the training chain we\ninfuse information from the training target example that we would like the\nchains to reach with a high probability. The thus learned transition operator\nis able to produce quality and varied samples in a small number of steps.\nExperiments show competitive results compared to the samples generated with a\nbasic Generative Adversarial Net","url_abs":"http://arxiv.org/abs/1703.06975v1","url_pdf":"http://arxiv.org/pdf/1703.06975v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"learning-to-generate-samples-from-noise","repo_url":"https://github.com/bordesf/Infusion","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1703.06975","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}