{"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-diverse-image-colorization","title":"Learning Diverse Image Colorization","arxiv_id":"1612.01958","date":"2016-12-06","proceeding":"CVPR 2017 7","authors":["Aditya Deshpande","Jiajun Lu","Mao-Chuang Yeh","Min Jin Chong","David Forsyth"],"abstract":"Colorization is an ambiguous problem, with multiple viable colorizations for\na single grey-level image. However, previous methods only produce the single\nmost probable colorization. Our goal is to model the diversity intrinsic to the\nproblem of colorization and produce multiple colorizations that display\nlong-scale spatial co-ordination. We learn a low dimensional embedding of color\nfields using a variational autoencoder (VAE). We construct loss terms for the\nVAE decoder that avoid blurry outputs and take into account the uneven\ndistribution of pixel colors. Finally, we build a conditional model for the\nmulti-modal distribution between grey-level image and the color field\nembeddings. Samples from this conditional model result in diverse colorization.\nWe demonstrate that our method obtains better diverse colorizations than a\nstandard conditional variational autoencoder (CVAE) model, as well as a\nrecently proposed conditional generative adversarial network (cGAN).","url_abs":"http://arxiv.org/abs/1612.01958v2","url_pdf":"http://arxiv.org/pdf/1612.01958v2.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-diverse-image-colorization","repo_url":"https://github.com/aditya12agd5/divcolor","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"colorization","task_name":"Colorization"},{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":null,"task_name":"Generative Adversarial Network"},{"task_slug":"image-colorization","task_name":"Image Colorization"}],"methods":[{"method_slug":"colorization","method_name":"Colorization"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1612.01958","atlas_url":"https://app.syntology.ai/?focus=1612.01958","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}