{"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/attribute-guided-unpaired-image-to-image","title":"Attribute Guided Unpaired Image-to-Image Translation with Semi-supervised Learning","arxiv_id":"1904.12428","date":"2019-04-29","proceeding":null,"authors":["Xinyang Li","Jie Hu","Shengchuan Zhang","Xiaopeng Hong","Qixiang Ye","Chenglin Wu","Rongrong Ji"],"abstract":"Unpaired Image-to-Image Translation (UIT) focuses on translating images among\ndifferent domains by using unpaired data, which has received increasing\nresearch focus due to its practical usage. However, existing UIT schemes defect\nin the need of supervised training, as well as the lack of encoding domain\ninformation. In this paper, we propose an Attribute Guided UIT model termed\nAGUIT to tackle these two challenges. AGUIT considers multi-modal and\nmulti-domain tasks of UIT jointly with a novel semi-supervised setting, which\nalso merits in representation disentanglement and fine control of outputs.\nEspecially, AGUIT benefits from two-fold: (1) It adopts a novel semi-supervised\nlearning process by translating attributes of labeled data to unlabeled data,\nand then reconstructing the unlabeled data by a cycle consistency operation.\n(2) It decomposes image representation into domain-invariant content code and\ndomain-specific style code. The redesigned style code embeds image style into\ntwo variables drawn from standard Gaussian distribution and the distribution of\ndomain label, which facilitates the fine control of translation due to the\ncontinuity of both variables. Finally, we introduce a new challenge, i.e.,\ndisentangled transfer, for UIT models, which adopts the disentangled\nrepresentation to translate data less related with the training set. Extensive\nexperiments demonstrate the capacity of AGUIT over existing state-of-the-art\nmodels.","url_abs":"http://arxiv.org/abs/1904.12428v1","url_pdf":"http://arxiv.org/pdf/1904.12428v1.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":"attribute-guided-unpaired-image-to-image","repo_url":"https://github.com/imlixinyang/AGUIT","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"disentanglement","task_name":"Disentanglement"},{"task_slug":"image-to-image-translation","task_name":"Image-to-Image Translation"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.12428","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}