{"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/semi-supervised-learning-with-gans-revisiting","title":"Semi-Supervised Learning with GANs: Revisiting Manifold Regularization","arxiv_id":"1805.08957","date":"2018-05-23","proceeding":null,"authors":["Bruno Lecouat","Chuan-Sheng Foo","Houssam Zenati","Vijay R. Chandrasekhar"],"abstract":"GANS are powerful generative models that are able to model the manifold of\nnatural images. We leverage this property to perform manifold regularization by\napproximating the Laplacian norm using a Monte Carlo approximation that is\neasily computed with the GAN. When incorporated into the feature-matching GAN\nof Improved GAN, we achieve state-of-the-art results for GAN-based\nsemi-supervised learning on the CIFAR-10 dataset, with a method that is\nsignificantly easier to implement than competing methods.","url_abs":"http://arxiv.org/abs/1805.08957v1","url_pdf":"http://arxiv.org/pdf/1805.08957v1.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":"semi-supervised-learning-with-gans-revisiting","repo_url":"https://github.com/bruno-31/GAN-manifold-regularization","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"semi-supervised-learning-with-gans-revisiting","repo_url":"https://github.com/UCI-ML-course-team/GAN-manifold-regularization-PyTorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1805.08957","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}