{"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/tangent-normal-adversarial-regularization-for","title":"Tangent-Normal Adversarial Regularization for Semi-supervised Learning","arxiv_id":"1808.06088","date":"2018-08-18","proceeding":"CVPR 2019 6","authors":["Bing Yu","Jingfeng Wu","Jinwen Ma","Zhanxing Zhu"],"abstract":"Compared with standard supervised learning, the key difficulty in\nsemi-supervised learning is how to make full use of the unlabeled data. A\nrecently proposed method, virtual adversarial training (VAT), smartly performs\nadversarial training without label information to impose a local smoothness on\nthe classifier, which is especially beneficial to semi-supervised learning. In\nthis work, we propose tangent-normal adversarial regularization (TNAR) as an\nextension of VAT by taking the data manifold into consideration. The proposed\nTNAR is composed by two complementary parts, the tangent adversarial\nregularization (TAR) and the normal adversarial regularization (NAR). In TAR,\nVAT is applied along the tangent space of the data manifold, aiming to enforce\nlocal invariance of the classifier on the manifold, while in NAR, VAT is\nperformed on the normal space orthogonal to the tangent space, intending to\nimpose robustness on the classifier against the noise causing the observed data\ndeviating from the underlying data manifold. Demonstrated by experiments on\nboth artificial and practical datasets, our proposed TAR and NAR complement\nwith each other, and jointly outperforms other state-of-the-art methods for\nsemi-supervised learning.","url_abs":"http://arxiv.org/abs/1808.06088v3","url_pdf":"http://arxiv.org/pdf/1808.06088v3.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":"tangent-normal-adversarial-regularization-for","repo_url":"https://github.com/uuujf/TNAR","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"tar","task_name":"TAR"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1808.06088","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}