{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T23:24:38Z","timestamp":1783553078294,"version":"3.55.0"},"reference-count":101,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2020AAA0109401"],"award-info":[{"award-number":["2020AAA0109401"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62006112"],"award-info":[{"award-number":["62006112"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61773198"],"award-info":[{"award-number":["61773198"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61921006"],"award-info":[{"award-number":["61921006"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NSF of Jiangsu Province","award":["BK20200313"],"award-info":[{"award-number":["BK20200313"]}]},{"name":"CCF-Baidu Open Fund","award":["2021PP15002000"],"award-info":[{"award-number":["2021PP15002000"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2023,3,1]]},"DOI":"10.1109\/tpami.2022.3179368","type":"journal-article","created":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T19:48:23Z","timestamp":1654112903000},"page":"3721-3737","source":"Crossref","is-referenced-by-count":31,"title":["Revisiting Unsupervised Meta-Learning via the Characteristics of Few-Shot Tasks"],"prefix":"10.1109","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1173-1880","authenticated-orcid":false,"given":"Han-Jia","family":"Ye","sequence":"first","affiliation":[{"name":"State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu","family":"Han","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"De-Chuan","family":"Zhan","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","article-title":"One shot learning of simple visual concepts","volume-title":"Proc. 33th Annu. Meeting Cogn. Sci. Soc.","author":"Lake"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1126\/science.aab3050"},{"key":"ref3","article-title":"Siamese neural networks for one-shot image recognition","volume-title":"Proc. ICML Deep Learn. Workshop","author":"Koch"},{"key":"ref4","first-page":"3630","article-title":"Matching networks for one shot learning","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Vinyals"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.2994749"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00407"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/3394171.3413832"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6957"},{"key":"ref9","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Finn"},{"key":"ref10","first-page":"4080","article-title":"Prototypical networks for few-shot learning","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Snell"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01091"},{"key":"ref12","article-title":"Optimization as a model for few-shot learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Ravi"},{"key":"ref13","article-title":"On first-order meta-learning algorithms","author":"Nichol","year":"2018"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00760"},{"key":"ref15","article-title":"Revisiting meta-learning as supervised learning","author":"Chao","year":"2020"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00755"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00883"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58568-6_16"},{"key":"ref19","first-page":"766","article-title":"Discriminative unsupervised feature learning with convolutional neural networks","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Dosovitskiy"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00393"},{"key":"ref21","first-page":"10 132","article-title":"Unsupervised meta-learning for few-shot image classification","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Khodadadeh"},{"key":"ref22","article-title":"Assume, augment and learn: Unsupervised few-shot meta-learning via random labels and data augmentation","author":"Antoniou","year":"2019"},{"key":"ref23","article-title":"Unsupervised few-shot learning via distribution shift-based augmentation","author":"Qin","year":"2020"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"ref25","first-page":"1597","article-title":"A simple framework for contrastive learning of visual representations","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Chen"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00459"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2006.79"},{"key":"ref30","article-title":"Meta-SGD: Learning to learn quickly for few shot learning","author":"Li","year":"2017"},{"key":"ref31","first-page":"2933","article-title":"Gradient-based meta-learning with learned layerwise metric and subspace","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Lee"},{"key":"ref32","first-page":"719","article-title":"TADAM: Task dependent adaptive metric for improved few-shot learning","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Oreshkin"},{"key":"ref33","article-title":"Meta-learning with latent embedding optimization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Rusu"},{"key":"ref34","first-page":"1","article-title":"Multimodal model-agnostic meta-learning via task-aware modulation","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Vuorio"},{"key":"ref35","article-title":"How to train your MAML to excel in few-shot classification","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Ye"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46466-4_37"},{"key":"ref37","first-page":"7032","article-title":"Learning to model the tail","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Wang"},{"key":"ref38","article-title":"Interventional few-shot learning","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Yue"},{"key":"ref39","first-page":"2252","article-title":"Few-shot learning through an information retrieval lens","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Triantafillou"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-10925-7_35"},{"key":"ref41","first-page":"177","article-title":"Transferable meta learning across domains","volume-title":"Proc. Conf. Uncertainty Artif. Intell.","author":"Kang"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00948"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-020-3055-1"},{"key":"ref44","article-title":"A closer look at few-shot classification","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Chen"},{"key":"ref45","article-title":"Meta-dataset: A dataset of datasets for learning to learn from few examples","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Triantafillou"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3160362"},{"key":"ref47","article-title":"Meta-learning for semi-supervised few-shot classification","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Ren"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17277"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01287"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3007511"},{"key":"ref51","article-title":"Learning to propagate labels: Transductive propagation network for few-shot learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Liu"},{"key":"ref52","first-page":"10 276","article-title":"Learning to self-train for semi-supervised few-shot classification","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Li"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00653"},{"key":"ref54","article-title":"Meta-learning update rules for unsupervised representation learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Metz"},{"key":"ref55","article-title":"Unsupervised learning via meta-learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Hsu"},{"key":"ref56","article-title":"Meta-GMVAE: Mixture of Gaussian VAE for unsupervised meta-learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Lee"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.107951"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.3389\/fncom.2020.00083"},{"key":"ref59","article-title":"Self-supervised prototypical transfer learning for few-shot classification","author":"Medina","year":"2020"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW53098.2021.00300"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00674"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.167"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46466-4_5"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.278"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46487-9_40"},{"key":"ref66","article-title":"Unsupervised representation learning by predicting image rotations","author":"Gidaris","year":"2018","journal-title":"Proc. Int. Conf. Learn. Representations"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58571-6_38"},{"key":"ref69","article-title":"Few-shot image classification via contrastive self-supervised learning","author":"Li","year":"2020"},{"key":"ref70","article-title":"Debiased contrastive learning","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Chuang"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/WACV51458.2022.00106"},{"key":"ref72","first-page":"5628","article-title":"A theoretical analysis of contrastive unsupervised representation learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Saunshi"},{"key":"ref73","article-title":"Heated-up softmax embedding","author":"Zhang","year":"2018"},{"key":"ref74","article-title":"SimpleShot: Revisiting nearest-neighbor classification for few-shot learning","author":"Wang","year":"2019"},{"key":"ref75","first-page":"22243","article-title":"Big self-supervised models are strong semi-supervised learners","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Chen"},{"key":"ref76","article-title":"Improved baselines with momentum contrastive learning","author":"Chen","year":"2020"},{"key":"ref77","first-page":"3825","article-title":"LGM-Net: Learning to generate matching networks for few-shot learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Li"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00049"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3018506"},{"key":"ref80","first-page":"15 509","article-title":"Learning representations by maximizing mutual information across views","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Bachman"},{"key":"ref81","article-title":"AutoAugment: Learning augmentation policies from data","author":"Cubuk","year":"2018"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00359"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58621-8_45"},{"key":"ref84","first-page":"6827","article-title":"What makes for good views for contrastive learning?","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Tian"},{"key":"ref85","first-page":"5998","article-title":"Attention is all you need","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Vaswani"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"ref87","first-page":"11 887","article-title":"Improving generalization in meta-learning via task augmentation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yao"},{"key":"ref88","first-page":"21798","article-title":"Hard negative mixing for contrastive learning","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Kalantidis"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i2.20119"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-69535-4_3"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3013379"},{"key":"ref92","article-title":"Layer normalization","volume":"abs\/1607.06450","author":"Ba","year":"2016","journal-title":"CoRR"},{"issue":"1","key":"ref93","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11671"},{"key":"ref95","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ioffe"},{"key":"ref96","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref97","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kingma"},{"key":"ref98","article-title":"Redesigning the classification layer by randomizing the class representation vectors","author":"Shalev","year":"2020"},{"key":"ref99","doi-asserted-by":"publisher","DOI":"10.5244\/C.30.87"},{"key":"ref100","article-title":"The Caltech-UCSD Birds-200\u20132011 dataset","author":"Wah","year":"2011"},{"key":"ref101","first-page":"3391","article-title":"Deep sets","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Zaheer"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/10036240\/09786650.pdf?arnumber=9786650","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,11]],"date-time":"2024-07-11T17:53:38Z","timestamp":1720720418000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9786650\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,1]]},"references-count":101,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2022.3179368","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":[[2023,3,1]]}}}