{"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/triangle-generative-adversarial-networks","title":"Triangle Generative Adversarial Networks","arxiv_id":"1709.06548","date":"2017-09-19","proceeding":"NeurIPS 2017 12","authors":["Zhe Gan","Liqun Chen","Wei-Yao Wang","Yunchen Pu","Yizhe Zhang","Hao liu","Chunyuan Li","Lawrence Carin"],"abstract":"A Triangle Generative Adversarial Network ($\\Delta$-GAN) is developed for\nsemi-supervised cross-domain joint distribution matching, where the training\ndata consists of samples from each domain, and supervision of domain\ncorrespondence is provided by only a few paired samples. $\\Delta$-GAN consists\nof four neural networks, two generators and two discriminators. The generators\nare designed to learn the two-way conditional distributions between the two\ndomains, while the discriminators implicitly define a ternary discriminative\nfunction, which is trained to distinguish real data pairs and two kinds of fake\ndata pairs. The generators and discriminators are trained together using\nadversarial learning. Under mild assumptions, in theory the joint distributions\ncharacterized by the two generators concentrate to the data distribution. In\nexperiments, three different kinds of domain pairs are considered, image-label,\nimage-image and image-attribute pairs. Experiments on semi-supervised image\nclassification, image-to-image translation and attribute-based image generation\ndemonstrate the superiority of the proposed approach.","url_abs":"http://arxiv.org/abs/1709.06548v2","url_pdf":"http://arxiv.org/pdf/1709.06548v2.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":"triangle-generative-adversarial-networks","repo_url":"https://github.com/liqunchen0606/triangle-gan","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":null,"task_name":"Generative Adversarial Network"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"image-to-image-translation","task_name":"Image-to-Image Translation"},{"task_slug":"semi-supervised-image-classification","task_name":"Semi-Supervised Image Classification"},{"task_slug":"translation","task_name":"Translation"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1709.06548","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}