{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T07:33:46Z","timestamp":1723016026105},"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":[[2021,8]]},"abstract":"<jats:p>Partial-label learning (PLL) generally focuses on inducing a noise-tolerant multi-class classifier by training on overly-annotated samples, each of which is annotated with a set of labels, but only one is the valid label. A basic promise of existing PLL solutions is that there are sufficient partial-label (PL) samples for training. However, it is more common than not to have just few PL samples at hand when dealing with new tasks. Furthermore, existing few-shot learning algorithms assume precise labels of the support set; as such,  irrelevant labels may seriously mislead the meta-learner  and thus lead to a compromised performance. How to enable PLL under a few-shot learning setting is an important problem, but not yet well studied. In this paper, we introduce an approach called FsPLL (Few-shot PLL).  FsPLL first performs adaptive distance metric learning by an embedding network and rectifying prototypes on the tasks previously encountered. Next, it calculates the prototype of each class of a new task in the embedding network. An unseen example can then be classified via its distance to each prototype. Experimental results on widely-used few-shot datasets demonstrate that our FsPLL can achieve a superior performance than the state-of-the-art methods, and it needs fewer samples for quickly adapting to new tasks.<\/jats:p>","DOI":"10.24963\/ijcai.2021\/475","type":"proceedings-article","created":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T11:00:49Z","timestamp":1628679649000},"page":"3448-3454","source":"Crossref","is-referenced-by-count":4,"title":["Few-Shot Partial-Label Learning"],"prefix":"10.24963","author":[{"given":"Yunfeng","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Software Engineering, Shandong University, Jinan, Shandong, China"},{"name":"Joint SDU-NTU Centre for Artificial Intelligence Research, Shandong University, Jinan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoxian","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Shandong University, Jinan, Shandong, China"},{"name":"Joint SDU-NTU Centre for Artificial Intelligence Research, Shandong University, Jinan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Shandong University, Jinan, Shandong, China"},{"name":"Joint SDU-NTU Centre for Artificial Intelligence Research, Shandong University, Jinan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongmin","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Shandong University, Jinan, Shandong, China"},{"name":"Joint SDU-NTU Centre for Artificial Intelligence Research, Shandong University, Jinan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lizhen","family":"Cui","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Shandong University, Jinan, Shandong, China"},{"name":"Joint SDU-NTU Centre for Artificial Intelligence Research, Shandong University, Jinan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carlotta","family":"Domeniconi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, George Mason University, VA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"30","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2021","name":"Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}","start":{"date-parts":[[2021,8,19]]},"theme":"Artificial Intelligence","location":"Montreal, Canada","end":{"date-parts":[[2021,8,27]]}},"container-title":["Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T11:03:34Z","timestamp":1628679814000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2021\/475"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2021,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2021\/475","relation":{},"subject":[],"published":{"date-parts":[[2021,8]]}}}