{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T19:15:40Z","timestamp":1772824540004,"version":"3.50.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2023,12,13]],"date-time":"2023-12-13T00:00:00Z","timestamp":1702425600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,13]],"date-time":"2023-12-13T00:00:00Z","timestamp":1702425600000},"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":["61802197"],"award-info":[{"award-number":["61802197"]}],"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":[[2024,3]]},"DOI":"10.1007\/s00521-023-09318-x","type":"journal-article","created":{"date-parts":[[2023,12,13]],"date-time":"2023-12-13T20:01:25Z","timestamp":1702497685000},"page":"4451-4465","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Cross-domain few-shot learning based on feature adaptive distillation"],"prefix":"10.1007","volume":"36","author":[{"given":"Dingwei","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Hui","family":"Yan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4448-2617","authenticated-orcid":false,"given":"Yadang","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Dichao","family":"Li","sequence":"additional","affiliation":[]},{"given":"Chuanyan","family":"Hao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,13]]},"reference":[{"key":"9318_CR1","doi-asserted-by":"crossref","unstructured":"Wang H, Li Y (2022) Task-specific method-agnostic metric for few-shot learning. Neural Comput Appl, 1\u201310","DOI":"10.1007\/s00521-022-07858-2"},{"key":"9318_CR2","unstructured":"Li Y, Li H, Chen H, Chen C (2021) Hierarchical representation based query-specific prototypical network for few-shot image classification. arXiv preprint arXiv:2103.11384"},{"key":"9318_CR3","unstructured":"Finn C, Abbeel P, Levine S (2017) Model-agnostic meta-learning for fast adaptation of deep networks. In: International Conference on Machine Learning, pp 1126\u20131135. PMLR"},{"key":"9318_CR4","doi-asserted-by":"crossref","unstructured":"Li W-H, Liu X, Bilen H (2022) Cross-domain few-shot learning with task-specific adapters. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 7161\u20137170","DOI":"10.1109\/CVPR52688.2022.00702"},{"issue":"6","key":"9318_CR5","doi-asserted-by":"publisher","first-page":"4733","DOI":"10.1007\/s00521-021-06627-x","volume":"34","author":"L Sa","year":"2022","unstructured":"Sa L, Yu C, Ma X, Zhao X, Xie T (2022) Attentive fine-grained recognition for cross-domain few-shot classification. Neural Comput Appl 34(6):4733\u20134746","journal-title":"Neural Comput Appl"},{"key":"9318_CR6","doi-asserted-by":"publisher","first-page":"11573","DOI":"10.1007\/s00521-021-05847-5","volume":"33","author":"B Zhang","year":"2021","unstructured":"Zhang B, Wang B, Wu X, Zhang L, Yang M, Sun X (2021) Domain adaptive detection system for concealed objects using millimeter wave images. Neural Comput Appl 33:11573\u201311588","journal-title":"Neural Comput Appl"},{"key":"9318_CR7","doi-asserted-by":"publisher","first-page":"4483","DOI":"10.1007\/s00521-020-05275-x","volume":"33","author":"X Cao","year":"2021","unstructured":"Cao X, Wang Y, Chen B, Zeng N (2021) Domain-adaptive intelligence for fault diagnosis based on deep transfer learning from scientific test rigs to industrial applications. Neural Comput Appl 33:4483\u20134499","journal-title":"Neural Comput Appl"},{"key":"9318_CR8","unstructured":"Tseng HY, Lee HY, Huang JB, Yang MH (2020) Cross-domain few-shot classification via learned feature-wise transformation. arXiv preprint arXiv:2001.08735"},{"key":"9318_CR9","unstructured":"Zhou K, Yang Y, Qiao Y, Xiang T (2021) Domain generalization with mixstyle. arXiv preprint arXiv:2104.02008"},{"key":"9318_CR10","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\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XIV 16, pp. 266\u2013282. Springer","DOI":"10.1007\/978-3-030-58568-6_16"},{"issue":"12","key":"9318_CR11","doi-asserted-by":"publisher","first-page":"12745","DOI":"10.1109\/TCYB.2021.3088519","volume":"52","author":"Q Ye","year":"2021","unstructured":"Ye Q, Huang P, Zhang Z, Zheng Y, Fu L, Yang W (2021) Multiview learning with robust double-sided twin SVM. IEEE Trans Cybernet 52(12):12745\u201312758","journal-title":"IEEE Trans Cybernet"},{"issue":"1","key":"9318_CR12","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1109\/TNNLS.2020.3027588","volume":"33","author":"L Fu","year":"2020","unstructured":"Fu L, Li Z, Ye Q, Yin H, Liu Q, Chen X, Fan X, Yang W, Yang G (2020) Learning robust discriminant subspace based on joint l2, p- and l2, s-norm distance metrics. IEEE Trans Neural Netw Learn Syst 33(1):130\u2013144","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"9318_CR13","unstructured":"Triantafillou E, Zhu T, Dumoulin V, Lamblin P, Evci U, Xu K, Goroshin R, Gelada C, Swersky K, Manzagol P-A, et al (2019) Meta-dataset: A dataset of datasets for learning to learn from few examples. arXiv preprint arXiv:1903.03096"},{"issue":"8","key":"9318_CR14","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-021-3396-7","volume":"65","author":"Y Chen","year":"2022","unstructured":"Chen Y, Hao C, Yang ZX, Wu E (2022) Fast target-aware learning for few-shot video object segmentation. Sci China Inf Sci 65(8):182104","journal-title":"Sci China Inf Sci"},{"issue":"2","key":"9318_CR15","doi-asserted-by":"publisher","first-page":"296","DOI":"10.3390\/sym14020296","volume":"14","author":"F Wang","year":"2022","unstructured":"Wang F, Chai G, Li Q, Wang C (2022) An efficient deep unsupervised domain adaptation for unknown malware detection. Symmetry 14(2):296","journal-title":"Symmetry"},{"key":"9318_CR16","doi-asserted-by":"crossref","unstructured":"Bateni P, Goyal R, Masrani V, Wood F, Sigal L (2020) Improved few-shot visual classification. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 14493\u201314502","DOI":"10.1109\/CVPR42600.2020.01450"},{"key":"9318_CR17","unstructured":"Requeima J, Gordon J, Bronskill J, Nowozin S, Turner RE (2019) Fast and flexible multi-task classification using conditional neural adaptive processes. Adv Neural Inf Process Syst 32"},{"key":"9318_CR18","doi-asserted-by":"crossref","unstructured":"Dvornik N, Schmid C, Mairal J (2020) Selecting relevant features from a multi-domain representation for few-shot classification. In: Computer vision\u2013ECCV 2020: 16th European conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part X 16, pp. 769\u2013786. Springer","DOI":"10.1007\/978-3-030-58607-2_45"},{"key":"9318_CR19","unstructured":"Liu L, Hamilton W, Long G, Jiang J, Larochelle H (2020) A universal representation transformer layer for few-shot image classification. arXiv preprint arXiv:2006.11702"},{"key":"9318_CR20","unstructured":"Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531"},{"key":"9318_CR21","unstructured":"Liu L, Wang H, Lin J, Socher R, Xiong C (2019) Mkd: a multi-task knowledge distillation approach for pretrained language models. arXiv preprint arXiv:1911.03588"},{"key":"9318_CR22","doi-asserted-by":"crossref","unstructured":"You S, Xu C, Xu C, Tao D (2017) Learning from multiple teacher networks. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp 1285\u20131294","DOI":"10.1145\/3097983.3098135"},{"key":"9318_CR23","unstructured":"Tan X, Ren Y, He D, Qin T, Zhao Z, Liu TY (2019) Multilingual neural machine translation with knowledge distillation. arXiv preprint arXiv:1902.10461"},{"key":"9318_CR24","unstructured":"Vinyals O, Blundell C, Lillicrap T, Wierstra D, et al (2016) Matching networks for one shot learning. Adv Neural Inf Process Syst 29"},{"key":"9318_CR25","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"9318_CR26","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis 115:211\u2013252","journal-title":"Int J Comput Vis"},{"issue":"6266","key":"9318_CR27","doi-asserted-by":"publisher","first-page":"1332","DOI":"10.1126\/science.aab3050","volume":"350","author":"BM Lake","year":"2015","unstructured":"Lake BM, Salakhutdinov R, Tenenbaum JB (2015) Human-level concept learning through probabilistic program induction. Science 350(6266):1332\u20131338","journal-title":"Science"},{"key":"9318_CR28","unstructured":"Maji S, Rahtu E, Kannala J, Blaschko M, Vedaldi A (2013) Fine-grained visual classification of aircraft. arXiv preprint arXiv:1306.5151"},{"key":"9318_CR29","unstructured":"Wah C, Branson S, Welinder P, Perona P, Belongie S (2011) The caltech-ucsd birds-200-2011 dataset"},{"key":"9318_CR30","doi-asserted-by":"crossref","unstructured":"Cimpoi M, Maji S, Kokkinos I, Mohamed S, Vedaldi A (2014) Describing textures in the wild. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3606\u20133613","DOI":"10.1109\/CVPR.2014.461"},{"key":"9318_CR31","unstructured":"Jongejan J, Rowley H, Kawashima T, Kim J, Fox-Gieg N (2016) The quick, draw!-ai experiment. Mount View, CA, Accessed Feb 17(2018), 4"},{"key":"9318_CR32","unstructured":"Schroeder B, Cui Y (2018) Fgvcx fungi classification challenge 2018. Available online: https:\/\/github.com\/visipedia\/fgvcx\/_fungi\/_comp (Accessed on 14 July 2021)"},{"key":"9318_CR33","doi-asserted-by":"crossref","unstructured":"Nilsback M-E, Zisserman A (2008) Automated flower classification over a large number of classes. In: 2008 Sixth Indian conference on computer vision, graphics & image processing, pp 722\u2013729. IEEE","DOI":"10.1109\/ICVGIP.2008.47"},{"key":"9318_CR34","doi-asserted-by":"crossref","unstructured":"Houben S, Stallkamp J, Salmen J, Schlipsing M, Igel C (2013) Detection of traffic signs in real-world images: the german traffic sign detection benchmark. In: The 2013 international joint conference on neural networks (IJCNN), pp. 1\u20138. IEEE","DOI":"10.1109\/IJCNN.2013.6706807"},{"key":"9318_CR35","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Doll\u00e1r P, Zitnick CL (2014) Microsoft coco: Common objects in context. In: Computer vision\u2013ECCV 2014: 13th European conference, Zurich, Switzerland, 2014, proceedings, Part V 13, pp 740\u2013755. Springer","DOI":"10.1007\/978-3-319-10602-1_48"},{"issue":"11","key":"9318_CR36","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278\u20132324","journal-title":"Proc IEEE"},{"key":"9318_CR37","unstructured":"Krizhevsky A, Hinton G, et al (2009) Learning multiple layers of features from tiny images"},{"key":"9318_CR38","doi-asserted-by":"crossref","unstructured":"Li W-H, Liu X, Bilen H (2021) Universal representation learning from multiple domains for few-shot classification. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 9526\u20139535","DOI":"10.1109\/ICCV48922.2021.00939"},{"key":"9318_CR39","unstructured":"Finn C, Abbeel P, Levine S (2017) Model-agnostic meta-learning for fast adaptation of deep networks. In: International conference on machine learning, pp 1126\u20131135. PMLR"},{"key":"9318_CR40","doi-asserted-by":"crossref","unstructured":"Bateni P, Barber J, Van\u00a0de Meent JW, Wood F (2022) Enhancing few-shot image classification with unlabelled examples. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision, pp. 2796\u20132805","DOI":"10.1109\/WACV51458.2022.00166"},{"key":"9318_CR41","unstructured":"Triantafillou E, Larochelle H, Zemel R, Dumoulin V (2021) Learning a universal template for few-shot dataset generalization. In: International conference on machine learning, pp 10424\u201310433. PMLR"},{"key":"9318_CR42","doi-asserted-by":"crossref","unstructured":"Liu Y, Lee J, Zhu L, Chen L, Shi H, Yang Y (2021) A multi-mode modulator for multi-domain few-shot classification. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 8453\u20138462","DOI":"10.1109\/ICCV48922.2021.00834"},{"key":"9318_CR43","doi-asserted-by":"crossref","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision, pp 618\u2013626","DOI":"10.1109\/ICCV.2017.74"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09318-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-09318-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09318-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,12]],"date-time":"2024-02-12T10:17:48Z","timestamp":1707733068000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-09318-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,13]]},"references-count":43,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["9318"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-09318-x","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,13]]},"assertion":[{"value":"13 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 November 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 December 2023","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 known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}