{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T21:12:24Z","timestamp":1762377144547,"version":"3.37.3"},"reference-count":74,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"12","license":[{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100000923","name":"Australian Research Council Projects","doi-asserted-by":"publisher","award":["DP-180103424"],"award-info":[{"award-number":["DP-180103424"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000923","name":"Australian Research Council Projects","doi-asserted-by":"publisher","award":["FL-170100117","IC-190100031"],"award-info":[{"award-number":["FL-170100117","IC-190100031"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1109\/tnnls.2022.3162747","type":"journal-article","created":{"date-parts":[[2022,5,4]],"date-time":"2022-05-04T19:51:50Z","timestamp":1651693910000},"page":"9952-9965","source":"Crossref","is-referenced-by-count":7,"title":["SIN: Semantic Inference Network for Few-Shot Streaming Label Learning"],"prefix":"10.1109","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5350-4569","authenticated-orcid":false,"given":"Zhen","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science, Faculty of Engineering, The University of Sydney, Darlington, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8128-2788","authenticated-orcid":false,"given":"Liu","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Faculty of Engineering, The University of Sydney, Darlington, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1517-994X","authenticated-orcid":false,"given":"Yiqun","family":"Duan","sequence":"additional","affiliation":[{"name":"School of Computer Science, University of Technology Sydney, Ultimo, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dacheng","family":"Tao","sequence":"additional","affiliation":[{"name":"School of Computer Science, Faculty of Engineering, The University of Sydney, Darlington, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-45049-1_27"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/2674026.2674028"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-016-6906-3"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/860435.860495"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1126\/science.aam8332"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/2185354.2185358"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v38i3.2756"},{"key":"ref8","first-page":"378","article-title":"Deep streaming label learning","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Wang"},{"key":"ref9","article-title":"Streaming label learning for modeling labels on the fly","author":"You","year":"2016","journal-title":"arXiv:1604.05449"},{"key":"ref10","first-page":"1","article-title":"How to train your MAML","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Antoniou"},{"key":"ref11","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","volume":"70","author":"Finn"},{"key":"ref12","article-title":"On the importance of attention in meta-learning for few-shot text classification","author":"Jiang","year":"2018","journal-title":"arXiv:1806.00852"},{"key":"ref13","article-title":"On first-order meta-learning algorithms","author":"Nichol","year":"2018","journal-title":"arXiv:1803.02999"},{"key":"ref14","first-page":"1","article-title":"Meta-learning with latent embedding optimization","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Rusu"},{"key":"ref15","first-page":"4077","article-title":"Prototypical networks for few-shot learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Snell"},{"key":"ref16","first-page":"4847","article-title":"Adaptive cross-modal few-shot learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Xing"},{"key":"ref17","first-page":"5275","article-title":"Incremental few-shot learning with attention attractor networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Ren"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/0010-0277(87)90026-6"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1162\/1064546053278973"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1111\/1467-9280.03439"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3394171.3413718"},{"key":"ref22","first-page":"1","article-title":"A closer look at few-shot classification","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Chen"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2006.79"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4615-5529-2_8"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2984710"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2957187"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3011526"},{"key":"ref28","article-title":"Meta-baseline: Exploring simple meta-learning for few-shot learning","author":"Chen","year":"2020","journal-title":"arXiv:2003.04390"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00131"},{"key":"ref30","first-page":"3630","article-title":"Matching networks for one shot learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Vinyals"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00459"},{"key":"ref32","first-page":"9537","article-title":"Probabilistic model-agnostic meta-learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Finn"},{"key":"ref33","first-page":"3082","article-title":"Reviving and improving recurrent back-propagation","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Liao"},{"key":"ref34","first-page":"1","article-title":"Learning to propagate labels: Transductive propagation network for few-shot learning","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Liu"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00011"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00671"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.495"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.235"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1352"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/s0079-7421(08)60536-8"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.587"},{"key":"ref42","first-page":"1","article-title":"Learning to learn without forgetting by maximizing transfer and minimizing interference","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Riemer"},{"key":"ref43","first-page":"1","article-title":"Three continual learning scenarios","volume-title":"Proc. NeurIPS Continual Learn. Workshop","author":"van de Ven"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00854"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00119"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01220"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i3.16334"},{"key":"ref48","first-page":"1","article-title":"Multimodal deep learning","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Ngiam"},{"key":"ref49","first-page":"935","article-title":"Zero-shot learning through cross-modal transfer","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Socher"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i14.17541"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/365"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6868"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01246-5_24"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/8996.003.0015"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1137\/16M1080173"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1145\/3386252"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46379-7_1"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-20212-4"},{"key":"ref59","first-page":"53","article-title":"Effective and efficient multilabel classification in domains with large number of labels","volume-title":"Proc. ECML\/PKDD Workshop Mining Multidimensional Data (MMD)","author":"Tsoumakas"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1145\/1460096.1460104"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-87481-2_4"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2013.39"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2935143"},{"key":"ref64","first-page":"3780","article-title":"A unified view of multi-label performance measures","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","volume":"70","author":"Wu"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.199"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.2200\/s00832ed1v01y201802aim037"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1162"},{"key":"ref68","first-page":"8026","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","volume":"32","author":"Paszke"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1214\/cbms\/1462061091"},{"issue":"3","key":"ref70","first-page":"416","article-title":"Applications of empirical process theory","volume":"51","author":"van de Geer","year":"2002","journal-title":"J. Roy. Stat. Soc. D, Statistician"},{"volume-title":"Statistical Learning Theory","year":"1998","author":"Vapnik","key":"ref71"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/72.788640"},{"key":"ref73","first-page":"1","article-title":"On the discrimination-generalization tradeoff in GANs","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Zhang"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-30164-8"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/10336252\/09768155.pdf?arnumber=9768155","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,16]],"date-time":"2024-02-16T19:08:02Z","timestamp":1708110482000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9768155\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12]]},"references-count":74,"journal-issue":{"issue":"12"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2022.3162747","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"type":"print","value":"2162-237X"},{"type":"electronic","value":"2162-2388"}],"subject":[],"published":{"date-parts":[[2023,12]]}}}