{"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/a-viable-framework-for-semi-supervised","title":"A viable framework for semi-supervised learning on realistic dataset","arxiv_id":null,"date":"2022-07-21","proceeding":"Machine Learning 2022 7","authors":["Hao Chang","Guochen Xie","Jun Yu","Qiang Ling","Fang Gao","Ye Yu"],"abstract":"Semi-supervised Fine-Grained Recognition is a challenging task due to the difficulty of data imbalance, high inter-class similarity and domain mismatch. Recently, this field has witnessed giant leap and many methods have gained great performance. We discover that these existing Semi-supervised Learning (SSL) methods achieve satisfactory performance owe to the exploration of unlabeled data. However, on the realistic large-scale datasets, due to the abovementioned challenges, the improvement of the quality of pseudo-labels requires further research. In this work, we propose Bilateral-Branch Self-Training Framework (BiSTF), a simple yet effective framework to improve existing semi-supervised learning methods on class-imbalanced and domain-shifted fine-grained data. By adjusting stochastic epoch update frequency, BiSTF iteratively retrains a baseline SSL model with a labeled set expanded by selectively adding pseudo-labeled samples from an unlabeled set, where the distribution of pseudo-labeled samples is the same as the labeled data. We show that BiSTF outperforms the existing state-of-the-art SSL algorithm on Semi-iNat dataset. Our code is available at https://github.com/HowieChangchn/BiSTF.","url_abs":"https://link.springer.com/article/10.1007/s10994-022-06208-6","url_pdf":"https://link.springer.com/article/10.1007/s10994-022-06208-6","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":"a-viable-framework-for-semi-supervised","repo_url":"https://github.com/2023-MindSpore-1/ms-code-136","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"a-viable-framework-for-semi-supervised","repo_url":"https://github.com/HowieChangchn/A-viable-framework-for-semi-supervised-learning-on-realistic-dataset","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}