{"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-residual-images-for-face-attribute","title":"Learning Residual Images for Face Attribute Manipulation","arxiv_id":"1612.05363","date":"2016-12-16","proceeding":"CVPR 2017 7","authors":["Wei Shen","Rujie Liu"],"abstract":"Face attributes are interesting due to their detailed description of human\nfaces. Unlike prior researches working on attribute prediction, we address an\ninverse and more challenging problem called face attribute manipulation which\naims at modifying a face image according to a given attribute value. Instead of\nmanipulating the whole image, we propose to learn the corresponding residual\nimage defined as the difference between images before and after the\nmanipulation. In this way, the manipulation can be operated efficiently with\nmodest pixel modification. The framework of our approach is based on the\nGenerative Adversarial Network. It consists of two image transformation\nnetworks and a discriminative network. The transformation networks are\nresponsible for the attribute manipulation and its dual operation and the\ndiscriminative network is used to distinguish the generated images from real\nimages. We also apply dual learning to allow transformation networks to learn\nfrom each other. Experiments show that residual images can be effectively\nlearned and used for attribute manipulations. The generated images remain most\nof the details in attribute-irrelevant areas.","url_abs":"http://arxiv.org/abs/1612.05363v2","url_pdf":"http://arxiv.org/pdf/1612.05363v2.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-residual-images-for-face-attribute","repo_url":"https://github.com/Juzov/FaceAttributeManipulation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":null,"task_name":"Generative Adversarial Network"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1612.05363","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}