{"type":"ScholarlyArticle","title":"Few-Shot Partial-Label Learning","authors":[{"name":"Yunfeng Zhao","slug":"yunfeng-zhao","given":"Yunfeng","family":"Zhao","affiliation":""},{"name":"Guoxian Yu","slug":"guoxian-yu","given":"Guoxian","family":"Yu","affiliation":""},{"name":"Lei Liu","slug":"lei-liu","given":"Lei","family":"Liu","affiliation":""},{"name":"Zhongmin Yan","slug":"zhongmin-yan","given":"Zhongmin","family":"Yan","affiliation":""},{"name":"Lizhen Cui","slug":"lizhen-cui","given":"Lizhen","family":"Cui","affiliation":""},{"name":"Carlotta Domeniconi","slug":"carlotta-domeniconi","given":"Carlotta","family":"Domeniconi","affiliation":""}],"abstract":"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.","doi":"10.24963/ijcai.2021/475","url":"https://ijcai.org/proceedings/2021/475","pdf":"https://ijcai.org/proceedings/2021/0475.pdf","markdown":"https://ijcai.org/proceedings/2021/475.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/475","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Machine Learning","track":"Main Track","number":475,"pages":{"first":3448,"last":3454},"keywords":["Machine Learning: Weakly Supervised Learning","Machine Learning: Multi-instance; Multi-label; Multi-view learning","Machine Learning: Transfer, Adaptation, Multi-task Learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}