{"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/elegant-exchanging-latent-encodings-with-gan","title":"ELEGANT: Exchanging Latent Encodings with GAN for Transferring Multiple Face Attributes","arxiv_id":"1803.10562","date":"2018-03-28","proceeding":"ECCV 2018 9","authors":["Taihong Xiao","Jiapeng Hong","Jinwen Ma"],"abstract":"Recent studies on face attribute transfer have achieved great success. A lot\nof models are able to transfer face attributes with an input image. However,\nthey suffer from three limitations: (1) incapability of generating image by\nexemplars; (2) being unable to transfer multiple face attributes\nsimultaneously; (3) low quality of generated images, such as low-resolution or\nartifacts. To address these limitations, we propose a novel model which\nreceives two images of opposite attributes as inputs. Our model can transfer\nexactly the same type of attributes from one image to another by exchanging\ncertain part of their encodings. All the attributes are encoded in a\ndisentangled manner in the latent space, which enables us to manipulate several\nattributes simultaneously. Besides, our model learns the residual images so as\nto facilitate training on higher resolution images. With the help of\nmulti-scale discriminators for adversarial training, it can even generate\nhigh-quality images with finer details and less artifacts. We demonstrate the\neffectiveness of our model on overcoming the above three limitations by\ncomparing with other methods on the CelebA face database. A pytorch\nimplementation is available at https://github.com/Prinsphield/ELEGANT.","url_abs":"http://arxiv.org/abs/1803.10562v2","url_pdf":"http://arxiv.org/pdf/1803.10562v2.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":"elegant-exchanging-latent-encodings-with-gan","repo_url":"https://github.com/Prinsphield/ELEGANT","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"elegant-exchanging-latent-encodings-with-gan","repo_url":"https://github.com/Prinsphield/DNA-GAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"attribute","task_name":"Attribute"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1803.10562","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.10562"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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