{"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-deep-learning-for-fully","title":"Semi-Supervised Deep Learning for Fully Convolutional Networks","arxiv_id":"1703.06000","date":"2017-03-17","proceeding":null,"authors":["Christoph Baur","Shadi Albarqouni","Nassir Navab"],"abstract":"Deep learning usually requires large amounts of labeled training data, but\nannotating data is costly and tedious. The framework of semi-supervised\nlearning provides the means to use both labeled data and arbitrary amounts of\nunlabeled data for training. Recently, semi-supervised deep learning has been\nintensively studied for standard CNN architectures. However, Fully\nConvolutional Networks (FCNs) set the state-of-the-art for many image\nsegmentation tasks. To the best of our knowledge, there is no existing\nsemi-supervised learning method for such FCNs yet. We lift the concept of\nauxiliary manifold embedding for semi-supervised learning to FCNs with the help\nof Random Feature Embedding. In our experiments on the challenging task of MS\nLesion Segmentation, we leverage the proposed framework for the purpose of\ndomain adaptation and report substantial improvements over the baseline model.","url_abs":"http://arxiv.org/abs/1703.06000v2","url_pdf":"http://arxiv.org/pdf/1703.06000v2.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-deep-learning-for-fully","repo_url":"https://github.com/bumuckl/SemiSupervisedDLForFCNs","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"lesion-segmentation","task_name":"Lesion Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}