{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T01:45:32Z","timestamp":1780105532719,"version":"3.54.0"},"reference-count":70,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"8","license":[{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100007219","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62476087"],"award-info":[{"award-number":["62476087"]}],"id":[{"id":"10.13039\/100007219","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key Research and Development Program of China","award":["2022YFB3203500"],"award-info":[{"award-number":["2022YFB3203500"]}]},{"name":"Shanghai Science and Technology Program \u201cFederated Based Cross-Domain and Cross-Task Incremental Learning\u201d","award":["21511100800"],"award-info":[{"award-number":["21511100800"]}]},{"DOI":"10.13039\/501100017674","name":"Chinese Defense Program of Science and Technology","doi-asserted-by":"publisher","award":["2021-JCJQ-JJ-0041"],"award-info":[{"award-number":["2021-JCJQ-JJ-0041"]}],"id":[{"id":"10.13039\/501100017674","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":[[2025,8]]},"DOI":"10.1109\/tnnls.2024.3519750","type":"journal-article","created":{"date-parts":[[2024,12,25]],"date-time":"2024-12-25T14:32:02Z","timestamp":1735137122000},"page":"14843-14854","source":"Crossref","is-referenced-by-count":2,"title":["Transductive Parameter-Free Propagation Framework for Few-Shot Distribution Rectification"],"prefix":"10.1109","volume":"36","author":[{"given":"Heng","family":"Tian","sequence":"first","affiliation":[{"name":"Ministry of Education, and the Department of Computer Science and Engineering, Key Laboratory of Smart Manufacturing in Energy Chemical Process, East China University of Science and Technology, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3984-5532","authenticated-orcid":false,"given":"Ziqiu","family":"Chi","sequence":"additional","affiliation":[{"name":"Ministry of Education, and the Department of Computer Science and Engineering, Key Laboratory of Smart Manufacturing in Energy Chemical Process, East China University of Science and Technology, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3759-2041","authenticated-orcid":false,"given":"Zhe","family":"Wang","sequence":"additional","affiliation":[{"name":"Ministry of Education, and the Department of Computer Science and Engineering, Key Laboratory of Smart Manufacturing in Energy Chemical Process, East China University of Science and Technology, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1215-7876","authenticated-orcid":false,"given":"Wei","family":"Guo","sequence":"additional","affiliation":[{"name":"Ministry of Education, and the Department of Computer Science and Engineering, Key Laboratory of Smart Manufacturing in Energy Chemical Process, East China University of Science and Technology, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengping","family":"Yang","sequence":"additional","affiliation":[{"name":"Ministry of Education, and the Department of Computer Science and Engineering, Key Laboratory of Smart Manufacturing in Energy Chemical Process, East China University of Science and Technology, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6645-859X","authenticated-orcid":false,"given":"Xinlei","family":"Xu","sequence":"additional","affiliation":[{"name":"Ministry of Education, and the Department of Computer Science and Engineering, Key Laboratory of Smart Manufacturing in Energy Chemical Process, East China University of Science and Technology, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.4324\/9781410605337-29"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/3386252"},{"key":"ref3","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","volume-title":"Proc. 34th Int. Conf. Mach. Learn.","author":"Finn"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00010"},{"key":"ref5","first-page":"1","article-title":"Learning to propagate labels: Transductive propagation network for few-shot learning","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Liu"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2021.3088545"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3179368"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.5555\/3294996.3295163"},{"key":"ref9","first-page":"1","article-title":"A closer look at few-shot classification","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Chen"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3204684"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58568-6_16"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/WACV45572.2020.9093338"},{"key":"ref13","first-page":"25767","article-title":"A closer look at prototype classifier for few-shot image classification","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Hou"},{"key":"ref14","first-page":"11660","article-title":"Laplacian regularized few-shot learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ziko"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58452-8_43"},{"key":"ref16","first-page":"1","article-title":"Free lunch for few-shot learning: Distribution calibration","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Yang"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR48806.2021.9412076"},{"key":"ref18","first-page":"2212","article-title":"Parameterless transductive feature re-representation for few-shot learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Cui"},{"key":"ref19","first-page":"1","article-title":"A theoretical analysis of the number of shots in few-shot learning","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Cao"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-013-0706-y"},{"key":"ref21","first-page":"10276","article-title":"Learning to self-train for semi-supervised few-shot classification","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Li"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.3115\/981658.981684"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58574-7_8"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00886"},{"key":"ref25","first-page":"6098","article-title":"Unsupervised embedding adaptation via early-stage feature reconstruction for few-shot classification","volume-title":"Proc. Int. Conf. Mach. Learn.","volume":"139","author":"Lee"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00855"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01485"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01285"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP39728.2021.9413783"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i2.16252"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"ref32","first-page":"1","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Kipf"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00063"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2022.3173687"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3184813"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3244023"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i1.25128"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2024.3355774"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01085"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2024.3411452"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i7.28614"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/398"},{"key":"ref43","article-title":"Transductive few-shot learning: Clustering is all you need?","author":"Masud Ziko","year":"2021","journal-title":"arXiv:2106.09516"},{"key":"ref44","first-page":"1","article-title":"Meta-learning with latent embedding optimization","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Rusu"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01351"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1016\/S0016-0032(96)00063-4"},{"key":"ref47","volume-title":"Information Theory and Statistics","author":"Kullback","year":"1997"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00370"},{"key":"ref49","article-title":"SimpleShot: Revisiting nearest-neighbor classification for few-shot learning","author":"Wang","year":"2019","journal-title":"arXiv:1911.04623"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3241919"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110746"},{"key":"ref52","first-page":"1","article-title":"Empirical Bayes transductive meta-learning with synthetic gradients","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Hu"},{"key":"ref53","first-page":"3630","article-title":"Matching networks for one shot learning","volume-title":"Proc. Conf. Workshop Neural Inf. Process. Syst.","author":"Vinyals"},{"key":"ref54","first-page":"1","article-title":"Meta-learning for semi-supervised few-shot classification","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Ren"},{"key":"ref55","article-title":"The caltech-ucsd birds-200\u20132011 dataset","author":"Wah","year":"2011"},{"key":"ref56","first-page":"1","article-title":"Meta-learning with differentiable closed-form solvers","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Bertinetto"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref58","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20819"},{"key":"ref60","article-title":"LEAF: A benchmark for federated settings","author":"Caldas","year":"2018","journal-title":"arXiv:1812.01097"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.5244\/C.30.87"},{"key":"ref63","first-page":"1","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Kingma"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2023.3298303"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.3233\/IDA-2002-6504"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2021.3125129"},{"key":"ref67","first-page":"2445","article-title":"Information maximization for few-shot learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Boudiaf"},{"key":"ref68","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. 20th Int. Conf. Artif. Intell. Statist.","volume":"54","author":"McMahan"},{"issue":"86","key":"ref69","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/5962385\/11114436\/10815613.pdf?arnumber=10815613","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T18:01:20Z","timestamp":1754503280000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10815613\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8]]},"references-count":70,"journal-issue":{"issue":"8"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2024.3519750","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8]]}}}