{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T16:24:05Z","timestamp":1781713445341,"version":"3.54.5"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"14","license":[{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52072026"],"award-info":[{"award-number":["52072026"]}],"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":["62076022"],"award-info":[{"award-number":["62076022"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2023,5]]},"DOI":"10.1007\/s00521-022-07607-5","type":"journal-article","created":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T04:02:26Z","timestamp":1662004946000},"page":"10069-10082","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A transfer-based few-shot classification approach via masked manifold mixup and fuzzy memory contrastive learning"],"prefix":"10.1007","volume":"35","author":[{"given":"Runliang","family":"Tian","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongmei","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,9,1]]},"reference":[{"issue":"7553","key":"7607_CR1","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436\u2013444","journal-title":"Nature"},{"issue":"1","key":"7607_CR2","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1109\/TNNLS.2016.2616906","volume":"29","author":"X-J Li","year":"2018","unstructured":"Li X-J, Yang G-H (2018) Neural-network-based adaptive decentralized fault-tolerant control for a class of interconnected nonlinear systems. IEEE Trans Neural Netw Learn Syst 29(1):144\u2013155","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"1","key":"7607_CR3","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1109\/TCYB.2015.2399334","volume":"46","author":"X-J Li","year":"2016","unstructured":"Li X-J, Yang G-H (2016) Fls-based adaptive synchronization control of complex dynamical networks with nonlinear couplings and state-dependent uncertainties. IEEE Trans Cybern 46(1):171\u2013180","journal-title":"IEEE Trans Cybern"},{"key":"7607_CR4","doi-asserted-by":"publisher","first-page":"700","DOI":"10.28991\/esj-2021-01306","volume":"5","author":"M Kowsher","year":"2021","unstructured":"Kowsher M, Hossen I, Tahabilder A, Prottasha N, Habib K, Azmi Z (2021) Support directional shifting vector: a direction based machine learning classifier. Emerg Sci J 5:700\u2013713","journal-title":"Emerg Sci J"},{"key":"7607_CR5","unstructured":"Dai Z, Liu H, Le QV, Tan M (2021) CoAtNet: marrying convolution and attention for all data sizes. In: Advances in neural information processing systems"},{"key":"7607_CR6","doi-asserted-by":"crossref","unstructured":"Pham H, Dai Z, Xie Q, Le QV (2021) Meta pseudo labels. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 11557\u201311568","DOI":"10.1109\/CVPR46437.2021.01139"},{"key":"7607_CR7","unstructured":"Foret P, Kleiner A, Mobahi H, Neyshabur B (2021) Sharpness-aware minimization for efficiently improving generalization. In: International conference on learning representations"},{"key":"7607_CR8","doi-asserted-by":"publisher","first-page":"200","DOI":"10.28991\/esj-2021-01270","volume":"5","author":"S Yeruva","year":"2021","unstructured":"Yeruva S, Varalakshmi M, Gowtham B, Chandana Y, Prasad P (2021) Identification of sickle cell anemia using deep neural networks. Emerg Sci J 5:200\u2013210","journal-title":"Emerg Sci J"},{"key":"7607_CR9","doi-asserted-by":"publisher","first-page":"15","DOI":"10.28991\/HIJ-2022-03-01-02","volume":"3","author":"H Ziafati Bagherzadeh","year":"2022","unstructured":"Ziafati Bagherzadeh H, Toosizadeh S (2022) Eye tracking algorithm based on multi model Kalman filter. HighTech Innov J 3:15\u201327","journal-title":"HighTech Innov J"},{"issue":"6","key":"7607_CR10","doi-asserted-by":"publisher","first-page":"4483","DOI":"10.1007\/s10462-021-10004-4","volume":"54","author":"M Huisman","year":"2021","unstructured":"Huisman M, van Rijn JN, Plaat A (2021) A survey of deep meta-learning. Artif Intell Rev 54(6):4483\u20134541","journal-title":"Artif Intell Rev"},{"key":"7607_CR11","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1016\/j.neucom.2020.05.114","volume":"456","author":"X Li","year":"2021","unstructured":"Li X, Sun Z, Xue J-H, Ma Z (2021) A concise review of recent few-shot meta-learning methods. Neurocomputing 456:463\u2013468","journal-title":"Neurocomputing"},{"key":"7607_CR12","unstructured":"Finn C, Abbeel P, Levine S (2017) Model-agnostic meta-learning for fast adaptation of deep networks. In: Proceedings of the 34th international conference on machine learning, vol 70, pp 1126\u20131135"},{"key":"7607_CR13","unstructured":"Rusu AA, Rao D, Sygnowski J, Vinyals O, Pascanu R, Osindero S, Hadsell R (2019) Meta-learning with latent embedding optimization. In: International conference on learning representations"},{"key":"7607_CR14","unstructured":"Raghu A, Raghu M, Bengio S, Vinyals O (2020) Rapid learning or feature reuse? Towards understanding the effectiveness of MAML. In: International conference on learning representations"},{"key":"7607_CR15","doi-asserted-by":"crossref","unstructured":"Wang Y-X, Girshick R, Hebert M, Hariharan B (2018) Low-shot learning from imaginary data. In: Proceedings of the IEEE conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2018.00760"},{"key":"7607_CR16","unstructured":"Schwartz E, Karlinsky L, Shtok J, Harary S, Marder M, Kumar A, Feris R, Giryes R, Bronstein A (2018) Delta-encoder: an effective sample synthesis method for few-shot object recognition. In: Advances in neural information processing systems, vol 31"},{"key":"7607_CR17","unstructured":"Zhang R, Che T, Ghahramani Z, Bengio Y, Song Y (2018) MetaGAN: an adversarial approach to few-shot learning. In: Advances in neural information processing systems, vol 31"},{"key":"7607_CR18","doi-asserted-by":"crossref","unstructured":"Zhang H, Zhang J, Koniusz P (2019) Few-shot learning via saliency-guided hallucination of samples. In: 2019 IEEE\/CVF conference on computer vision and pattern recognition, pp 2765\u20132774","DOI":"10.1109\/CVPR.2019.00288"},{"key":"7607_CR19","doi-asserted-by":"crossref","unstructured":"Chen Z, Fu Y, Wang Y-X, Ma L, Liu W, Hebert M (2019) Image deformation meta-networks for one-shot learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2019.00888"},{"key":"7607_CR20","unstructured":"Chen W-Y, Liu Y-C, Kira Z, Wang Y-CF, Huang J-B (2019) A closer look at few-shot classification. In: International conference on learning representations"},{"key":"7607_CR21","doi-asserted-by":"crossref","unstructured":"Mangla P, Kumari N, Sinha A, Singh M, Krishnamurthy B, Balasubramanian VN (2020) Charting the right manifold: manifold mixup for few-shot learning. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision","DOI":"10.1109\/WACV45572.2020.9093338"},{"key":"7607_CR22","doi-asserted-by":"crossref","unstructured":"Hu Y, Gripon V, Pateux S (2021) Leveraging the feature distribution in transfer-based few-shot learning. In: Artificial neural networks and machine learning\u2014ICANN 2021, pp 487\u2013499","DOI":"10.1007\/978-3-030-86340-1_39"},{"key":"7607_CR23","unstructured":"Verma V, Lamb A, Beckham C, Najafi A, Mitliagkas I, Lopez-Paz D, Bengio Y (2019) Manifold mixup: better representations by interpolating hidden states. In: Proceedings of the 36th international conference on machine learning, vol 97, pp 6438\u20136447"},{"key":"7607_CR24","unstructured":"Vinyals O, Blundell C, Lillicrap T, kavukcuoglu k, Wierstra D (2016) Matching networks for one shot learning. In: Advances in neural information processing systems, vol 29"},{"key":"7607_CR25","unstructured":"Snell J, Swersky K, Zemel R (2017) Prototypical networks for few-shot learning. In: Advances in neural information processing systems, vol 30"},{"key":"7607_CR26","doi-asserted-by":"crossref","unstructured":"Sung F, Yang Y, Zhang L, Xiang T, Torr PHS, Hospedales TM (2018) Learning to compare: relation network for few-shot learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2018.00131"},{"key":"7607_CR27","doi-asserted-by":"crossref","unstructured":"Tian Y, Wang Y, Krishnan D, Tenenbaum JB, Isola P (2020) Rethinking few-shot image classification: a good embedding is all you need? In: Computer vision\u2014ECCV 2020, pp 266\u2013282","DOI":"10.1007\/978-3-030-58568-6_16"},{"key":"7607_CR28","doi-asserted-by":"crossref","unstructured":"Gidaris S, Bursuc A, Komodakis N, Perez P, Cord M (2019) Boosting few-shot visual learning with self-supervision. In: Proceedings of the IEEE\/CVF international conference on computer vision","DOI":"10.1109\/ICCV.2019.00815"},{"key":"7607_CR29","unstructured":"Gidaris S, Singh P, Komodakis N (2018) Unsupervised representation learning by predicting image rotations. In: International conference on learning representations"},{"issue":"56","key":"7607_CR30","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(56):1929\u20131958","journal-title":"J Mach Learn Res"},{"key":"7607_CR31","unstructured":"Ghiasi G, Lin T-Y, Le QV (2018) DropBlock: a regularization method for convolutional networks. In: Advances in neural information processing systems, vol 31"},{"key":"7607_CR32","doi-asserted-by":"crossref","unstructured":"Zhong Z, Zheng L, Kang G, Li S, Yang Y (2020) Random erasing data augmentation. In: Proceedings of the AAAI conference on artificial intelligence (AAAI)","DOI":"10.1609\/aaai.v34i07.7000"},{"key":"7607_CR33","unstructured":"Zhang H, Cisse M, Dauphin YN, Lopez-Paz D (2018) mixup: beyond empirical risk minimization. In: International conference on learning representations"},{"key":"7607_CR34","unstructured":"Hendrycks D, Mu N, Cubuk ED, Zoph B, Gilmer J, Lakshminarayanan B (2020) AugMix: a simple method to improve robustness and uncertainty under data shift. In: International conference on learning representations"},{"key":"7607_CR35","doi-asserted-by":"crossref","unstructured":"Yun S, Han D, Oh SJ, Chun S, Choe J, Yoo Y (2019) Cutmix: regularization strategy to train strong classifiers with localizable features. In: Proceedings of the IEEE\/CVF international conference on computer vision (ICCV)","DOI":"10.1109\/ICCV.2019.00612"},{"key":"7607_CR36","unstructured":"Cascante-Bonilla P, Sekhon A, Qi Y, Ordonez V (2021) Evolving image compositions for feature representation learning. In: British machine vision conference (BMVC)"},{"key":"7607_CR37","doi-asserted-by":"crossref","unstructured":"Ye M, Zhang X, Yuen PC, Chang S-F (2019) Unsupervised embedding learning via invariant and spreading instance feature. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2019.00637"},{"key":"7607_CR38","unstructured":"Chen T, Kornblith S, Norouzi M, Hinton G (2020) A simple framework for contrastive learning of visual representations. In: Proceedings of the 37th international conference on machine learning. proceedings of machine learning research, vol 119, pp 1597\u20131607"},{"key":"7607_CR39","doi-asserted-by":"crossref","unstructured":"Wu Z, Xiong Y, Yu SX, Lin D (2018) Unsupervised feature learning via non-parametric instance discrimination. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2018.00393"},{"key":"7607_CR40","unstructured":"Caron M, Misra I, Mairal J, Goyal P, Bojanowski P, Joulin A (2020) Unsupervised learning of visual features by contrasting cluster assignments. In: Advances in neural information processing systems, vol 33, pp 9912\u20139924"},{"key":"7607_CR41","doi-asserted-by":"crossref","unstructured":"He K, Fan H, Wu Y, Xie S, Girshick R (2020) Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"7607_CR42","doi-asserted-by":"crossref","unstructured":"Zagoruyko S, Komodakis N (2016) Wide residual networks. In: Proceedings of the British machine vision conference (BMVC), pp 1\u201312","DOI":"10.5244\/C.30.87"},{"key":"7607_CR43","unstructured":"Wah C, Branson S, Welinder P, Perona P, Belongie S (2011) The Caltech-UCSD Birds-200-2011 Dataset. Technical report CNS-TR-2011-001, California Institute of Technology"},{"key":"7607_CR44","unstructured":"Bertinetto L, Henriques JF, Torr PHS, Vedaldi A (2019) Meta learning with differentiable closed-form solvers. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition"},{"key":"7607_CR45","unstructured":"Kingma DP, Ba J (2015) Adam: a method for stochastic optimization. In: International conference on learning representations"},{"key":"7607_CR46","doi-asserted-by":"publisher","first-page":"3336","DOI":"10.1109\/TIP.2019.2959254","volume":"29","author":"D Das","year":"2020","unstructured":"Das D, Lee CSG (2020) A two-stage approach to few-shot learning for image recognition. IEEE Trans Image Process 29:3336\u20133350","journal-title":"IEEE Trans Image Process"},{"key":"7607_CR47","unstructured":"Mishra N, Rohaninejad M, Chen X, Abbeel P (2018) A simple neural attentive meta-learner. In: International conference on learning representations"},{"key":"7607_CR48","unstructured":"Dhillon GS, Chaudhari P, Ravichandran A, Soatto S (2020) A baseline for few-shot image classification. In: International conference on learning representations"},{"key":"7607_CR49","doi-asserted-by":"crossref","unstructured":"Lee K, Maji S, Ravichandran A, Soatto S (2019) Meta-learning with differentiable convex optimization. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2019.01091"},{"key":"7607_CR50","doi-asserted-by":"crossref","unstructured":"Chen D, Chen Y, Li Y, Mao F, He Y, Xue H (2021) Self-supervised learning for few-shot image classification. In: ICASSP 2021\u20132021 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 1745\u20131749","DOI":"10.1109\/ICASSP39728.2021.9413783"},{"key":"7607_CR51","doi-asserted-by":"crossref","unstructured":"Simon C, Koniusz P, Nock R, Harandi M (2020) Adaptive subspaces for few-shot learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR42600.2020.00419"},{"key":"7607_CR52","doi-asserted-by":"crossref","unstructured":"Ma Y, Bai S, An S, Liu W, Liu A, Zhen X, Liu X (2020) Transductive relation-propagation network for few-shot learning. In: Proceedings of the twenty-ninth international joint conference on artificial intelligence, IJCAI-20, pp 804\u2013810","DOI":"10.24963\/ijcai.2020\/112"},{"key":"7607_CR53","unstructured":"Hu SX, Moreno PG, Xiao Y, Shen X, Obozinski G, Lawrence N, Damianou A (2020) Empirical bayes transductive meta-learning with synthetic gradients. In: International conference on learning representations"},{"key":"7607_CR54","doi-asserted-by":"crossref","unstructured":"Liu J, Song L, Qin Y (2020) Prototype rectification for few-shot learning. In: Computer vision\u2014ECCV 2020, pp 741\u2013756","DOI":"10.1007\/978-3-030-58452-8_43"},{"key":"7607_CR55","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3082928","author":"Y Ma","year":"2021","unstructured":"Ma Y, Bai S, Liu W, Wang S, Yu Y, Bai X, Liu X, Wang M (2021) Transductive relation-propagation with decoupling training for few-shot learning. IEEE Trans Neural Netw Learn Syst. https:\/\/doi.org\/10.1109\/TNNLS.2021.3082928","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"7607_CR56","unstructured":"Ziko I, Dolz J, Granger E, Ayed IB (2020) Laplacian regularized few-shot learning. In: Proceedings of the 37th international conference on machine learning, vol 119, pp 11660\u201311670"},{"key":"7607_CR57","doi-asserted-by":"crossref","unstructured":"Hu Y, Gripon V, Pateux S (2021) Graph-based interpolation of feature vectors for accurate few-shot classification. In: 2020 25th international conference on pattern recognition, pp 8164\u20138171","DOI":"10.1109\/ICPR48806.2021.9412076"},{"key":"7607_CR58","doi-asserted-by":"crossref","unstructured":"Lichtenstein M, Sattigeri P, Feris R, Giryes R, Karlinsky L (2020) TAFSSL: task-adaptive feature sub-space learning for few-shot classification. In: Computer vision\u2014ECCV 2020, pp 522\u2013539","DOI":"10.1007\/978-3-030-58571-6_31"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07607-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07607-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07607-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,25]],"date-time":"2023-04-25T00:07:46Z","timestamp":1682381266000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07607-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,1]]},"references-count":58,"journal-issue":{"issue":"14","published-print":{"date-parts":[[2023,5]]}},"alternative-id":["7607"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07607-5","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,1]]},"assertion":[{"value":"17 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 July 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 September 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}