{"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/fast-and-generalized-adaptation-for-few-shot","title":"Generalized Adaptation for Few-Shot Learning","arxiv_id":"1911.10807","date":"2019-11-25","proceeding":null,"authors":["Liang Song","Jinlu Liu","Yongqiang Qin"],"abstract":"Many Few-Shot Learning research works have two stages: pre-training base model and adapting to novel model. In this paper, we propose to use closed-form base learner, which constrains the adapting stage with pre-trained base model to get better generalized novel model. Following theoretical analysis proves its rationality as well as indication of how to train a well-generalized base model. We then conduct experiments on four benchmarks and achieve state-of-the-art performance in all cases. Notably, we achieve the accuracy of 87.75% on 5-shot miniImageNet which approximately outperforms existing methods by 10%.","url_abs":"https://arxiv.org/abs/1911.10807v3","url_pdf":"https://arxiv.org/pdf/1911.10807v3.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":[],"tasks":[{"task_slug":"few-shot-image-classification","task_name":"Few-Shot Image Classification"},{"task_slug":"few-shot-learning","task_name":"Few-Shot Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/few-shot-image-classification-on-cifar-fs-5","task":"Few-Shot Image Classification","dataset":"CIFAR-FS 5-way (1-shot)","model":"ACC + Amphibian","rank_in_archive_order":31,"of":38,"metrics":{"Accuracy":"73.1"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-cifar-fs-5-1","task":"Few-Shot Image Classification","dataset":"CIFAR-FS 5-way (5-shot)","model":"ACC + Amphibian","rank_in_archive_order":14,"of":39,"metrics":{"Accuracy":"89.3"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-fc100-5-way","task":"Few-Shot Image Classification","dataset":"FC100 5-way (1-shot)","model":"ACC + Amphibian","rank_in_archive_order":19,"of":22,"metrics":{"Accuracy":"41.6"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-fc100-5-way-1","task":"Few-Shot Image Classification","dataset":"FC100 5-way (5-shot)","model":"ACC + Amphibian","rank_in_archive_order":4,"of":22,"metrics":{"Accuracy":"66.9"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-mini-2","task":"Few-Shot Image Classification","dataset":"Mini-Imagenet 5-way (1-shot)","model":"ACC + Amphibian","rank_in_archive_order":68,"of":105,"metrics":{"Accuracy":"62.21"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-mini-3","task":"Few-Shot Image Classification","dataset":"Mini-Imagenet 5-way (5-shot)","model":"ACC + Amphibian","rank_in_archive_order":50,"of":95,"metrics":{"Accuracy":"80.75"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-tiered","task":"Few-Shot Image Classification","dataset":"Tiered ImageNet 5-way (1-shot)","model":"ACC + Amphibian","rank_in_archive_order":38,"of":49,"metrics":{"Accuracy":"68.77"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-tiered-1","task":"Few-Shot Image Classification","dataset":"Tiered ImageNet 5-way (5-shot)","model":"ACC + Amphibian","rank_in_archive_order":23,"of":51,"metrics":{"Accuracy":"86.75"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1911.10807","atlas_url":"https://app.syntology.ai/?focus=1911.10807","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}