{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T05:36:00Z","timestamp":1768109760073,"version":"3.49.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,8]]},"abstract":"<jats:p>Zero-shot learning (ZSL) is to construct recognition models for unseen target classes that have no labeled samples for training. It utilizes the class attributes or semantic vectors as side information and transfers supervision information from related source classes with abundant labeled samples. Existing ZSL approaches adopt an intermediary embedding space to measure the similarity between a sample and the attributes of a target class to perform zero-shot classification. However, this way may suffer from the information loss caused by the embedding process and the similarity measure cannot fully make use of the data distribution. In this paper, we propose a novel approach which turns the ZSL problem into a conventional supervised learning problem by synthesizing samples for the unseen classes. Firstly, the probability distribution of an unseen class is estimated by using the knowledge from seen classes and the class attributes. Secondly, the samples are synthesized based on the distribution for the unseen class. Finally, we can train any supervised classifiers based on the synthesized samples. Extensive experiments on benchmarks demonstrate the superiority of the proposed approach to the state-of-the-art ZSL approaches.<\/jats:p>","DOI":"10.24963\/ijcai.2017\/246","type":"proceedings-article","created":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T05:14:07Z","timestamp":1501218847000},"page":"1774-1780","source":"Crossref","is-referenced-by-count":35,"title":["Synthesizing Samples for Zero-shot Learning"],"prefix":"10.24963","author":[{"given":"Yuchen","family":"Guo","sequence":"first","affiliation":[{"name":"Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guiguang","family":"Ding","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jungong","family":"Han","sequence":"additional","affiliation":[{"name":"School of Computing & Communications, Lancaster University, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Software, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"name":"Twenty-Sixth International Joint Conference on Artificial Intelligence","theme":"Artificial Intelligence","location":"Melbourne, Australia","acronym":"IJCAI-2017","number":"26","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)","University of Technology Sydney (UTS)","Australian Computer Society (ACS)"],"start":{"date-parts":[[2017,8,19]]},"end":{"date-parts":[[2017,8,26]]}},"container-title":["Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T07:52:59Z","timestamp":1501228379000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2017\/246"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2017,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2017\/246","relation":{},"subject":[],"published":{"date-parts":[[2017,8]]}}}