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In each training episode, an episodic class mean computed from a support set is registered with the global representation via a registration module. This produces a registered global class representation for computing the classification loss using a query set. Though following a similar episodic training pipeline as existing meta learning based approaches, our method differs significantly in that novel class training samples are involved in the training from the beginning. To compensate for the lack of novel class training samples, an effective sample synthesis strategy is developed to avoid overfitting. Importantly, by joint base-novel class training, our approach can be easily extended to a more practical yet challenging FSL setting, i.e., generalized FSL, where the label space of test data is extended to both base and novel classes. Extensive experiments show that our approach is effective for both of the two FSL settings.","url_abs":"https://arxiv.org/abs/1908.05257v1","url_pdf":"https://arxiv.org/pdf/1908.05257v1.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":"few-shot-learning-with-global-class","repo_url":"https://github.com/tiangeluo/fsl-global","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"few-shot-learning-with-global-class","repo_url":"https://github.com/huang-research-group/globalfsl2019","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"few-shot-image-classification","task_name":"Few-Shot Image Classification"},{"task_slug":"few-shot-learning","task_name":"Few-Shot Learning"},{"task_slug":"generalized-few-shot-classification","task_name":"Generalized Few-Shot Classification"},{"task_slug":"meta-learning","task_name":"Meta-Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/few-shot-image-classification-on-mini-1","task":"Few-Shot Image Classification","dataset":"Mini-ImageNet - 1-Shot Learning","model":"GCR","rank_in_archive_order":14,"of":16,"metrics":{"Accuracy":"53.21"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-omniglot-1-1","task":"Few-Shot Image Classification","dataset":"OMNIGLOT - 1-Shot, 20-way","model":"GCR","rank_in_archive_order":1,"of":20,"metrics":{"Accuracy":"99.63"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-omniglot-5-1","task":"Few-Shot Image Classification","dataset":"OMNIGLOT - 5-Shot, 20-way","model":"GCR","rank_in_archive_order":6,"of":19,"metrics":{"Accuracy":"99.32"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-mini-9","task":"Few-Shot Image Classification","dataset":"mini-ImageNet - 100-Way","model":"GCR","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy":"39.14"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1908.05257","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.05257"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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