{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T10:09:29Z","timestamp":1760609369700,"version":"3.37.3"},"reference-count":25,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,7,5]],"date-time":"2021-07-05T00:00:00Z","timestamp":1625443200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,7,5]],"date-time":"2021-07-05T00:00:00Z","timestamp":1625443200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,7,5]]},"DOI":"10.1109\/icme51207.2021.9428103","type":"proceedings-article","created":{"date-parts":[[2021,6,9]],"date-time":"2021-06-09T21:14:21Z","timestamp":1623273261000},"page":"1-6","source":"Crossref","is-referenced-by-count":6,"title":["A Generative Model for Partial Label Learning"],"prefix":"10.1109","author":[{"given":"Yan","family":"Yan","sequence":"first","affiliation":[{"name":"Northwestern Polytechnical University,School of Computer Science and Engineering,Xi&#x2019;an,China"}]},{"given":"Shining","family":"Li","sequence":"additional","affiliation":[{"name":"Northwestern Polytechnical University,School of Computer Science and Engineering,Xi&#x2019;an,China"}]}],"member":"263","reference":[{"key":"ref10","article-title":"Learning with multiple labels","author":"jin","year":"2003","journal-title":"NeurIPS"},{"key":"ref11","article-title":"Solving the partial label learning problem: An instance-based approach","author":"zhang","year":"2015","journal-title":"IJCAI"},{"key":"ref12","doi-asserted-by":"crossref","DOI":"10.1609\/aaai.v31i1.10775","article-title":"Confidence-rated discriminative partial label learning","author":"tang","year":"2017","journal-title":"AAAI"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/291"},{"key":"ref14","article-title":"Partial label learning with self-guided retraining","author":"lei","year":"2019","journal-title":"AAAI"},{"article-title":"Conditional generative adversarial nets","year":"2014","author":"mirza","key":"ref15"},{"key":"ref16","article-title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results","author":"tarvainen","year":"2017","journal-title":"NeurIPS"},{"article-title":"mixup: Beyond empirical risk minimization","year":"2017","author":"zhang","key":"ref17"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/398"},{"key":"ref19","article-title":"Partial label learning via label enhancement","author":"jia-qi","year":"2019","journal-title":"AAAI"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.3233\/IDA-2006-10503"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6132"},{"key":"ref6","article-title":"Learning from candidate labeling sets","author":"luo","year":"2010","journal-title":"NeurIPS"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.97"},{"key":"ref8","first-page":"1501","article-title":"Learning from partial labels","volume":"12","author":"cour","year":"2011","journal-title":"J Mach Learn Res"},{"key":"ref7","article-title":"Learnability of the superset label learning problem","author":"liu","year":"2014","journal-title":"ICML"},{"key":"ref2","article-title":"Partial label learning via generative adversarial nets","author":"zhang","year":"2020","journal-title":"ECAI"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939788"},{"key":"ref1","article-title":"Provably consistent partial-label learning","author":"feng","year":"2020","journal-title":"NeurIPS"},{"key":"ref20","article-title":"An overview of research activities in facial age estimation using the fgnet aging database","author":"panis","year":"2014","journal-title":"ECCV"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1613\/jair.105"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-15549-9_46"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1401958"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/2339530.2339616"},{"article-title":"Adam: A method for stochastic optimization","year":"2014","author":"kingma","key":"ref25"}],"event":{"name":"2021 IEEE International Conference on Multimedia and Expo (ICME)","start":{"date-parts":[[2021,7,5]]},"location":"Shenzhen, China","end":{"date-parts":[[2021,7,9]]}},"container-title":["2021 IEEE International Conference on Multimedia and Expo (ICME)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9428049\/9428068\/09428103.pdf?arnumber=9428103","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,30]],"date-time":"2022-12-30T13:57:16Z","timestamp":1672408636000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9428103\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,5]]},"references-count":25,"URL":"https:\/\/doi.org\/10.1109\/icme51207.2021.9428103","relation":{},"subject":[],"published":{"date-parts":[[2021,7,5]]}}}