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We propose to tackle this problem from the\nperspective of manifold learning. Our main idea is to align the semantic space\nthat is derived from external information to the model space that concerns\nitself with recognizing visual features. To this end, we introduce a set of\n\"phantom\" object classes whose coordinates live in both the semantic space and\nthe model space. Serving as bases in a dictionary, they can be optimized from\nlabeled data such that the synthesized real object classifiers achieve optimal\ndiscriminative performance. We demonstrate superior accuracy of our approach\nover the state of the art on four benchmark datasets for zero-shot learning,\nincluding the full ImageNet Fall 2011 dataset with more than 20,000 unseen\nclasses.","url_abs":"http://arxiv.org/abs/1603.00550v3","url_pdf":"http://arxiv.org/pdf/1603.00550v3.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":"synthesized-classifiers-for-zero-shot","repo_url":"https://github.com/JudyYe/zero-shot-gcn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"synthesized-classifiers-for-zero-shot","repo_url":"https://github.com/ruotianluo/zsl-gcn-pth","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"object","task_name":"Object"},{"task_slug":"zero-shot-learning","task_name":"Zero-Shot Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/few-shot-image-classification-on-awa-0-shot","task":"Few-Shot Image Classification","dataset":"AWA - 0-Shot","model":"Synthesised Classifier","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy":"72.9%"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-cub-200-2011-1","task":"Few-Shot Image Classification","dataset":"CUB-200-2011 - 0-Shot","model":"Synthesised Classifier","rank_in_archive_order":2,"of":5,"metrics":{"Top-1 Accuracy":"54.7%"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-imagenet-0","task":"Few-Shot Image Classification","dataset":"ImageNet - 0-Shot","model":"Synthesised Classifier","rank_in_archive_order":4,"of":5,"metrics":{"Accuracy":"1.5%"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-sun-0-shot","task":"Few-Shot Image Classification","dataset":"SUN - 0-Shot","model":"Synthesised Classifier","rank_in_archive_order":1,"of":2,"metrics":{"Accuracy":"62.7%"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1603.00550","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1603.00550"}},"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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