{"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/poodle-improving-few-shot-learning-via","title":"POODLE: Improving Few-shot Learning via Penalizing Out-of-Distribution Samples NeurIPS 2021","arxiv_id":null,"date":"2021-12-01","proceeding":"Advances in Neural Information Processing Systems 2021 12","authors":["Duong H. Le*","Khoi D. Nguyen*","Khoi Nguyen","Quoc-Huy Tran","Rang Nguyen","Binh-Son Hua"],"abstract":"In this work, we propose to use out-of-distribution samples, i.e., unlabeled samples coming from outside the target classes, to improve few-shot learning. Specifically, we exploit the easily available out-of-distribution samples to drive the classifier to avoid irrelevant features by maximizing the distance from prototypes to out-of-distribution samples while minimizing that of in-distribution samples (i.e., support, query data). Our approach is simple to implement, agnostic to feature extractors, lightweight without any additional cost for pre-training, and applicable to both inductive and transductive settings. Extensive experiments on various standard benchmarks demonstrate that the proposed method consistently improves the performance of pretrained networks with different architectures.","url_abs":"https://proceedings.neurips.cc/paper/2021/hash/c91591a8d461c2869b9f535ded3e213e-Abstract.html","url_pdf":"https://proceedings.neurips.cc/paper/2021/file/c91591a8d461c2869b9f535ded3e213e-Paper.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":"poodle-improving-few-shot-learning-via","repo_url":"https://github.com/lehduong/poodle","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","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}