{"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-few-shot-learning-with-local","title":"Semi-Supervised Few-Shot Learning with Prototypical Random Walks","arxiv_id":"1903.02164","date":"2019-03-06","proceeding":null,"authors":["Ahmed Ayyad","Yuchen Li","Nassir Navab","Shadi Albarqouni","Mohamed Elhoseiny"],"abstract":"Recent progress has shown that few-shot learning can be improved with access to unlabelled data, known as semi-supervised few-shot learning(SS-FSL). We introduce an SS-FSL approach, dubbed as Prototypical Random Walk Networks(PRWN), built on top of Prototypical Networks (PN). We develop a random walk semi-supervised loss that enables the network to learn representations that are compact and well-separated. Our work is related to the very recent development of graph-based approaches for few-shot learning. However, we show that compact and well-separated class representations can be achieved by modeling our prototypical random walk notion without needing additional graph-NN parameters or requiring a transductive setting where a collective test set is provided. Our model outperforms baselines in most benchmarks with significant improvements in some cases. Our model, trained with 40$\\%$ of the data as labeled, compares competitively against fully supervised prototypical networks, trained on 100$\\%$ of the labels, even outperforming it in the 1-shot mini-Imagenet case with 50.89$\\%$ to 49.4$\\%$ accuracy. We also show that our loss is resistant to distractors, unlabeled data that does not belong to any of the training classes, and hence reflecting robustness to labeled/unlabeled class distribution mismatch. Associated GitHub page can be found at https://prototypical-random-walk.github.io.","url_abs":"https://arxiv.org/abs/1903.02164v3","url_pdf":"https://arxiv.org/pdf/1903.02164v3.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-few-shot-learning-with-local","repo_url":"https://github.com/prototypical-random-walk/prototypical-random-walk","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"few-shot-learning","task_name":"Few-Shot Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}