{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T05:32:38Z","timestamp":1784007158131,"version":"3.55.0"},"reference-count":80,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"6","license":[{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"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","award":["61973162"],"award-info":[{"award-number":["61973162"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["30920032202"],"award-info":[{"award-number":["30920032202"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"CCF-Tencent Open Fund","award":["RAGR20200101"],"award-info":[{"award-number":["RAGR20200101"]}]},{"name":"CAST","award":["2018QNRC001"],"award-info":[{"award-number":["2018QNRC001"]}]},{"name":"Hong Kong Scholars Program","award":["XJ2019036"],"award-info":[{"award-number":["XJ2019036"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1713208"],"award-info":[{"award-number":["U1713208"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"111 Program","award":["AH92005MS"],"award-info":[{"award-number":["AH92005MS"]}]},{"DOI":"10.13039\/501100004377","name":"Hong Kong Polytechnic University","doi-asserted-by":"publisher","award":["YZ3K"],"award-info":[{"award-number":["YZ3K"]}],"id":[{"id":"10.13039\/501100004377","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004377","name":"Hong Kong Polytechnic University","doi-asserted-by":"publisher","award":["UAJP\/UAGK"],"award-info":[{"award-number":["UAJP\/UAGK"]}],"id":[{"id":"10.13039\/501100004377","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004377","name":"Hong Kong Polytechnic University","doi-asserted-by":"publisher","award":["ZVRH"],"award-info":[{"award-number":["ZVRH"]}],"id":[{"id":"10.13039\/501100004377","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["20H04206"],"award-info":[{"award-number":["20H04206"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2022,6,1]]},"DOI":"10.1109\/tpami.2020.3044997","type":"journal-article","created":{"date-parts":[[2020,12,15]],"date-time":"2020-12-15T21:40:58Z","timestamp":1608068458000},"page":"2841-2855","source":"Crossref","is-referenced-by-count":32,"title":["Centroid Estimation With Guaranteed Efficiency: A General Framework for Weakly Supervised Learning"],"prefix":"10.1109","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4092-9856","authenticated-orcid":false,"given":"Chen","family":"Gong","sequence":"first","affiliation":[{"name":"PCA Lab, Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu, P.R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4800-832X","authenticated-orcid":false,"given":"Jian","family":"Yang","sequence":"additional","affiliation":[{"name":"PCA Lab, Jiangsu Key Laboratory of Image and Video Understanding for Social Security, School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu, P.R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8181-4836","authenticated-orcid":false,"given":"Jane","family":"You","sequence":"additional","affiliation":[{"name":"Department of Computing, Hong Kong Polytechnic University, Hong Kong, P.R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6658-6743","authenticated-orcid":false,"given":"Masashi","family":"Sugiyama","sequence":"additional","affiliation":[{"name":"RIKEN Center for Advanced Intelligence Project, Tokyo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1093\/nsr\/nwx106"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-01548-9"},{"key":"ref3","first-page":"387","article-title":"Partially supervised classification of text documents","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Liu"},{"key":"ref4","first-page":"1","article-title":"On the minimal supervision for training any binary classifier from only unlabeled data","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Lu"},{"key":"ref5","article-title":"Active learning literature survey","author":"Settles","year":"2009"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"ref7","first-page":"577","article-title":"Support vector machines for multiple-instance learning","volume-title":"Proc. Advances Neural Inf. Process. Syst.","author":"Andrews"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206667"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2017.2669639"},{"key":"ref10","first-page":"5639","article-title":"Learning from complementary labels","volume-title":"Proc. Advances Neural Inf. Process. Syst.","author":"Ishida"},{"key":"ref11","first-page":"5917","article-title":"Binary classification from positive-confidence data","volume-title":"Proc. Advances Neural Inf. Process. Syst.","author":"Ishida"},{"key":"ref12","first-page":"1196","article-title":"Learning with noisy labels","volume-title":"Proc. Advances Neural Inf. Process. Syst.","author":"Natarajan"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1177\/1354856507084420"},{"issue":"1","key":"ref14","first-page":"2151","article-title":"Convex and scalable weakly labeled SVMs","volume":"14","author":"Li","year":"2013","journal-title":"J. Mach. Learn. Res."},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2922396"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.10293"},{"key":"ref17","first-page":"708","article-title":"Loss factorization, weakly supervised learning and label noise robustness","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Patrini"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2941684"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/4175.001.0001"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2892403"},{"key":"ref21","first-page":"1386","article-title":"Convex formulation for learning from positive and unlabeled data","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Du Plessis"},{"key":"ref22","first-page":"1674","article-title":"Positive-unlabeled learning with non-negative risk estimator","volume-title":"Proc. Advances Neural Inf. Process. Syst.","author":"Kiryo"},{"key":"ref23","first-page":"2998","article-title":"Semi-supervised classification based on classification from positive and unlabeled data","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Sakai"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11715"},{"key":"ref25","first-page":"489","article-title":"Classification with asymmetric label noise: Consistency and maximal denoising","volume-title":"Proc. Annu. Conf. Learn. Theory","author":"Scott"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2456899"},{"key":"ref27","first-page":"2052","article-title":"Mixture proportion estimation via kernel embeddings of distributions","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ramaswamy"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-016-5604-6"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/S0004-3702(96)00034-3"},{"key":"ref30","first-page":"912","article-title":"Semi-supervised learning using Gaussian fields and harmonic functions","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhu"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065703001522"},{"key":"ref32","first-page":"2399","article-title":"Manifold regularization: A geometric framework for learning from labeled and unlabeled examples","volume":"7","author":"Belkin","year":"2006","journal-title":"J. Mach. Learn. Res."},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2014.2376963"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2014.2376936"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2008.216"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2016.2514360"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2016.2563981"},{"key":"ref38","first-page":"1","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf"},{"key":"ref39","first-page":"200","article-title":"Transductive inference for text classification using support vector machines","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Joachims"},{"key":"ref40","first-page":"1687","article-title":"Large scale transductive SVMs","volume":"7","author":"Collobert","year":"2006","journal-title":"J. Mach. Learn. Res."},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553456"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2014.2299812"},{"key":"ref43","first-page":"1","article-title":"Temporal ensembling for semi-supervised learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Laine"},{"key":"ref44","first-page":"1195","article-title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results","volume-title":"Proc. Advances Neural Inf. Process. Syst.","author":"Tarvainen"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2858821"},{"key":"ref46","first-page":"5050","article-title":"Mixmatch: A holistic approach to semi-supervised learning","volume-title":"Proc. Advances Neural Inf. Process. Syst.","author":"Berthelot"},{"key":"ref47","first-page":"587","article-title":"Learning to classify texts using positive and unlabeled data","volume-title":"Proc. Int. Joint Conf. Artif. Intell.","author":"Li"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1401920"},{"key":"ref49","first-page":"703","article-title":"Analysis of learning from positive and unlabeled data","volume-title":"Proc. Advances Neural Inf. Process. Syst.","author":"Du Plessis"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1023\/A:1020258113913"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2003.1250918"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/373"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/590"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2019.2903563"},{"key":"ref55","first-page":"1073","article-title":"EM-DD: An improved multiple-instance learning technique","volume-title":"Proc. Advances Neural Inf. Process. Syst.","author":"Zhang"},{"key":"ref56","article-title":"Address instance-level label prediction in multiple instance learning","author":"Peng","year":"2019"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298968"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2017.08.026"},{"key":"ref59","first-page":"1119","article-title":"Solving multiple-instance problem: A lazy learning approach","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Wang"},{"key":"ref60","first-page":"179","article-title":"Multi-instance kernels","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"G\u00e4rtner"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2016.2519102"},{"key":"ref62","first-page":"3376","article-title":"Attention-based deep multiple instance learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ilse"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2019.07.071"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1613\/jair.606"},{"key":"ref65","first-page":"920","article-title":"Eliminating class noise in large datasets","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhu"},{"key":"ref66","first-page":"2304","article-title":"Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Jiang"},{"key":"ref67","first-page":"8527","article-title":"Co-teaching: Robust training of deep neural networks with extremely noisy labels","volume-title":"Proc. Advances Neural Inf. Process. Syst.","author":"Han"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00571"},{"key":"ref69","first-page":"1","article-title":"Curriculum loss: Robust learning and generalization against label corruption","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Lyu"},{"key":"ref70","first-page":"10","article-title":"Learning with symmetric label noise: The importance of being unhinged","volume-title":"Proc. Advances Neural Inf. Process. Syst.","author":"Rooyen"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.240"},{"key":"ref72","first-page":"6835","article-title":"Are anchor points really indispensable in label-noise learning?","volume-title":"Proc. Advances Neural Inf. Process. Syst.","author":"Xia"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46128-1_42"},{"key":"ref74","first-page":"4006","article-title":"SIGUA: Forgetting may make learning with noisy labels more robust","volume-title":"Proc. 37th Int. Conf. Mach. Learn.","author":"Han"},{"key":"ref75","article-title":"UCI machine learning repository","author":"Dua","year":"2017"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.3115\/981658.981684"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1145\/1273496.1273643"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/361"},{"key":"ref79","first-page":"452","article-title":"Classification from pairwise similarity and unlabeled data","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Bao"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-008-5084-4"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/9769881\/09294086.pdf?arnumber=9294086","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T23:10:38Z","timestamp":1704841838000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9294086\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,1]]},"references-count":80,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2020.3044997","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"value":"0162-8828","type":"print"},{"value":"2160-9292","type":"electronic"},{"value":"1939-3539","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,1]]}}}