{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,21]],"date-time":"2025-12-21T06:24:41Z","timestamp":1766298281625,"version":"3.37.3"},"reference-count":74,"publisher":"Springer Science and Business Media LLC","issue":"15","license":[{"start":{"date-parts":[[2023,1,26]],"date-time":"2023-01-26T00:00:00Z","timestamp":1674691200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,26]],"date-time":"2023-01-26T00:00:00Z","timestamp":1674691200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2023,5]]},"DOI":"10.1007\/s00521-023-08269-7","type":"journal-article","created":{"date-parts":[[2023,1,26]],"date-time":"2023-01-26T11:02:36Z","timestamp":1674730956000},"page":"10847-10860","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Learning cross-domain representations by vision transformer for unsupervised domain adaptation"],"prefix":"10.1007","volume":"35","author":[{"given":"Yifan","family":"Ye","sequence":"first","affiliation":[]},{"given":"Shuai","family":"Fu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5206-1110","authenticated-orcid":false,"given":"Jing","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,26]]},"reference":[{"key":"8269_CR1","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":"8269_CR2","first-page":"5998","volume":"30","author":"A Vaswani","year":"2017","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Adv Neural Inform Process Syst 30:5998\u20136008","journal-title":"Adv Neural Inform Process Syst"},{"key":"8269_CR3","first-page":"136","volume":"29","author":"M Long","year":"2016","unstructured":"Long M, Zhu H, Wang J, Jordan MI (2016) Unsupervised domain adaptation with residual transfer networks. Adv Neural Inform Process Syst 29:136\u2013144","journal-title":"Adv Neural Inform Process Syst"},{"key":"8269_CR4","unstructured":"Tzeng E, Hoffman J, Zhang N, Saenko K, Darrell T (2014) Deep domain confusion: maximizing for domain invariance. arXiv:1412.3474"},{"issue":"1","key":"8269_CR5","first-page":"2030","volume":"17","author":"Y Ganin","year":"2016","unstructured":"Ganin Y, Ustinova E, Ajakan H, Germain P, Larochelle H, Laviolette F, Marchand M, Lempitsky V (2016) Domain-adversarial training of neural networks. J Mach Learn Res 17(1):2030\u20132096","journal-title":"J Mach Learn Res"},{"key":"8269_CR6","unstructured":"Xie S, Zheng Z, Chen L, Chen C (2018) Learning semantic representations for unsupervised domain adaptation. In: Proceedings of the 35th international conference on machine learning, pp 5423\u20135432"},{"key":"8269_CR7","doi-asserted-by":"crossref","unstructured":"Gu X, Sun J, Xu Z (2020) Spherical space domain adaptation with robust pseudo-label loss. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 9101\u20139110","DOI":"10.1109\/CVPR42600.2020.00912"},{"key":"8269_CR8","doi-asserted-by":"publisher","first-page":"107309","DOI":"10.1016\/j.knosys.2021.107309","volume":"229","author":"N Xiao","year":"2021","unstructured":"Xiao N, Zhang L, Xu X, Guo T, Ma H (2021) Label disentangled analysis for unsupervised visual domain adaptation. Knowl-Based Syst 229:107309","journal-title":"Knowl-Based Syst"},{"key":"8269_CR9","doi-asserted-by":"publisher","first-page":"105344","DOI":"10.1016\/j.knosys.2019.105344","volume":"192","author":"Y Wang","year":"2020","unstructured":"Wang Y, Nie L, Li Y, Chen S (2020) Soft large margin clustering for unsupervised domain adaptation. Knowl-Based Syst 192:105344","journal-title":"Knowl-Based Syst"},{"key":"8269_CR10","doi-asserted-by":"crossref","unstructured":"Chen C, Chen Z, Jiang B, Jin X (2019) Joint domain alignment and discriminative feature learning for unsupervised deep domain adaptation. In: Proceedings of the AAAI conference on artificial intelligence, pp 3296\u20133303","DOI":"10.1609\/aaai.v33i01.33013296"},{"key":"8269_CR11","unstructured":"Long M, Zhu H, Wang J, Jordan MI (2017) Deep transfer learning with joint adaptation networks. In: Proceedings of the 34th international conference on machine learning, pp 2208\u20132217"},{"key":"8269_CR12","doi-asserted-by":"publisher","first-page":"106394","DOI":"10.1016\/j.knosys.2020.106394","volume":"207","author":"L Yang","year":"2020","unstructured":"Yang L, Zhong P (2020) Discriminative and informative joint distribution adaptation for unsupervised domain adaptation. Knowl-Based Syst 207:106394","journal-title":"Knowl-Based Syst"},{"key":"8269_CR13","doi-asserted-by":"publisher","first-page":"105155","DOI":"10.1016\/j.knosys.2019.105155","volume":"191","author":"H Wu","year":"2020","unstructured":"Wu H, Yan Y, Ye Y, Ng MK, Wu Q (2020) Geometric knowledge embedding for unsupervised domain adaptation. Knowl-Based Syst 191:105155","journal-title":"Knowl-Based Syst"},{"key":"8269_CR14","first-page":"139","volume":"63","author":"I Goodfellow","year":"2014","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. Adv Neural Inform Process Syst 63:139\u2013144","journal-title":"Adv Neural Inform Process Syst"},{"key":"8269_CR15","doi-asserted-by":"crossref","unstructured":"Tzeng E, Hoffman J, Saenko K, Darrell T (2017) Adversarial discriminative domain adaptation. In: IEEE conference on computer vision and pattern recognition, pp 2962\u20132971","DOI":"10.1109\/CVPR.2017.316"},{"key":"8269_CR16","doi-asserted-by":"crossref","unstructured":"Xu M, Zhang J, Ni B, Li T, Wang C, Tian Q, Zhang W (2020) Adversarial domain adaptation with domain mixup. In: Proceedings of the AAAI conference on artificial intelligence, pp 6502\u20136509","DOI":"10.1609\/aaai.v34i04.6123"},{"key":"8269_CR17","doi-asserted-by":"crossref","unstructured":"Kang G, Jiang L, Yang Y, Hauptmann AG (2019) Contrastive adaptation network for unsupervised domain adaptation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 4893\u20134902","DOI":"10.1109\/CVPR.2019.00503"},{"key":"8269_CR18","first-page":"435","volume":"32","author":"Q Zhang","year":"2019","unstructured":"Zhang Q, Zhang J, Liu W, Tao D (2019) Category anchor-guided unsupervised domain adaptation for semantic segmentation. Adv Neural Inform Process Syst 32:435\u2013445","journal-title":"Adv Neural Inform Process Syst"},{"key":"8269_CR19","doi-asserted-by":"publisher","first-page":"107011","DOI":"10.1016\/j.knosys.2021.107011","volume":"222","author":"Y Zhang","year":"2021","unstructured":"Zhang Y, Jing C, Lin H, Chen C, Huang Y, Ding X, Zou Y (2021) Hard class rectification for domain adaptation. Knowl-Based Syst 222:107011","journal-title":"Knowl-Based Syst"},{"key":"8269_CR20","unstructured":"Saito K, Ushiku Y, Harada T (2017) Asymmetric tri-training for unsupervised domain adaptation. In: Proceedings of the international conference on machine learning, pp 2988\u20132997"},{"key":"8269_CR21","doi-asserted-by":"crossref","unstructured":"Zou Y, Yu Z, Kumar B, Wang J (2018) Unsupervised domain adaptation for semantic segmentation via class-balanced self-training. In: Proceedings of the European conference on computer vision, pp 289\u2013305","DOI":"10.1007\/978-3-030-01219-9_18"},{"key":"8269_CR22","doi-asserted-by":"crossref","unstructured":"Zou Y, Yu Z, Liu X, Kumar B, Wang J (2019) Confidence regularized self-training. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 5982\u20135991","DOI":"10.1109\/ICCV.2019.00608"},{"key":"8269_CR23","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S et al (2021) An image is worth 16 \u00d7 16 words: transformers for image recognition at scale. In: Proceedings of the 9th international conference on learning representations"},{"key":"8269_CR24","unstructured":"Touvron H, Cord M, Douze M, Massa F, Sablayrolles A, J\u00e9gou H (2021) Training data-efficient image transformers & distillation through attention. In: Proceedings of the 38th international conference on machine learning, pp 10347\u201310357"},{"key":"8269_CR25","unstructured":"Naseer M, Ranasinghe K, Khan S, Hayat M, Khan FS, Yang M-H (2021) Intriguing properties of vision transformers, arXiv preprint arXiv:2105.10497"},{"key":"8269_CR26","unstructured":"Benz P, Ham S, Zhang C, Karjauv A, Kweon IS (2021) Adversarial robustness comparison of vision transformer and mlp-mixer to CNNS, arXiv preprint arXiv:2110.02797"},{"key":"8269_CR27","first-page":"18408","volume":"34","author":"B Zhang","year":"2021","unstructured":"Zhang B, Wang Y, Hou W, Wu H, Wang J, Okumura M, Shinozaki T (2021) Flexmatch: boosting semi-supervised learning with curriculum pseudo labeling. Adv Neural Inform Process Syst 34:18408\u201318419","journal-title":"Adv Neural Inform Process Syst"},{"key":"8269_CR28","doi-asserted-by":"crossref","unstructured":"Khan S, Naseer M, Hayat M, Zamir SW, Khan FS, Shah M (2021) Transformers in vision: a survey. ACM Comput Surv (CSUR)","DOI":"10.1145\/3505244"},{"key":"8269_CR29","doi-asserted-by":"crossref","unstructured":"Yuan L, Chen Y, Wang T, Yu W, Shi Y, Jiang Z-H, Tay FE, Feng J, Yan S (2021) Tokens-to-token vit: training vision transformers from scratch on imagenet. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 558\u2013567","DOI":"10.1109\/ICCV48922.2021.00060"},{"key":"8269_CR30","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 10012\u201310022","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"8269_CR31","doi-asserted-by":"crossref","unstructured":"Chen C-FR, Fan Q, Panda R (2021) Crossvit: cross-attention multi-scale vision transformer for image classification. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 357\u2013366","DOI":"10.1109\/ICCV48922.2021.00041"},{"key":"8269_CR32","doi-asserted-by":"crossref","unstructured":"Carion N, Massa F, Synnaeve G, Usunier N, Kirillov A, Zagoruyko S (2020) End-to-end object detection with transformers. In: Proceedings of the European conference on computer vision, pp 213\u2013229","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"8269_CR33","doi-asserted-by":"crossref","unstructured":"Zheng S, Lu J, Zhao H, Zhu X, Luo Z, Wang Y, Fu Y, Feng J, Xiang T, Torr PH et al (2021) Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 6881\u20136890","DOI":"10.1109\/CVPR46437.2021.00681"},{"key":"8269_CR34","doi-asserted-by":"crossref","unstructured":"He S, Luo H, Wang P, Wang F, Li H, Jiang W (2021) Transreid: transformer-based object re-identification. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 15013\u201315022","DOI":"10.1109\/ICCV48922.2021.01474"},{"key":"8269_CR35","unstructured":"El-Nouby A, Neverova N, Laptev I, J\u00e9gou H (2021) Training vision transformers for image retrieval, arXiv preprint arXiv:2102.05644"},{"key":"8269_CR36","doi-asserted-by":"crossref","unstructured":"Huang L, Tan J, Liu J, Yuan J (2020) Hand-transformer: non-autoregressive structured modeling for 3D hand pose estimation. In: Proceedings of the European conference on computer vision, pp 17\u201333","DOI":"10.1007\/978-3-030-58595-2_2"},{"key":"8269_CR37","first-page":"14745","volume":"34","author":"Y Jiang","year":"2021","unstructured":"Jiang Y, Chang S, Wang Z (2021) Transgan: two pure transformers can make one strong gan, and that can scale up. Adv Neural Inform Process Syst 34:14745\u201314758","journal-title":"Adv Neural Inform Process Syst"},{"key":"8269_CR38","doi-asserted-by":"crossref","unstructured":"d\u2019Ascoli S, Touvron H, Leavitt M, Morcos A, Biroli G, Sagun L (2021) Convit: improving vision transformers with soft convolutional inductive biases, arXiv preprint arXiv:2103.10697","DOI":"10.1088\/1742-5468\/ac9830"},{"key":"8269_CR39","doi-asserted-by":"crossref","unstructured":"Caron M, Touvron H, Misra I, J\u00e9gou H, Mairal J, Bojanowski P, Joulin A (2021) Emerging properties in self-supervised vision transformers. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 9650\u20139660","DOI":"10.1109\/ICCV48922.2021.00951"},{"key":"8269_CR40","unstructured":"Amini M-R, Gallinari P (2002) Semi-supervised logistic regression. In: Proceedings of the 15th European conference on artificial intelligence, pp 390\u2013394"},{"key":"8269_CR41","first-page":"529","volume":"17","author":"Y Grandvalet","year":"2004","unstructured":"Grandvalet Y, Bengio Y (2004) Semi-supervised learning by entropy minimization. Adv Neural Inform Process Syst 17:529\u2013536","journal-title":"Adv Neural Inform Process Syst"},{"key":"8269_CR42","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1080\/01621459.1975.10479874","volume":"70","author":"GJ McLachlan","year":"1975","unstructured":"McLachlan GJ (1975) Iterative reclassification procedure for constructing an asymptotically optimal rule of allocation in discriminant analysis. J Am Statist Assoc 70:365\u2013369","journal-title":"J Am Statist Assoc"},{"key":"8269_CR43","doi-asserted-by":"crossref","unstructured":"Rosenberg C, Hebert M, Schneiderman H (2005) Semi-supervised self-training of object detection models. In: Proceedings of the seventh IEEE workshops on application of computer vision, pp 29\u201336","DOI":"10.1109\/ACVMOT.2005.107"},{"key":"8269_CR44","unstructured":"Lee D-H et al (2013) Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. In: Workshop on challenges in representation learning, ICML, p 896"},{"key":"8269_CR45","first-page":"5049","volume":"32","author":"D Berthelot","year":"2019","unstructured":"Berthelot D, Carlini N, Goodfellow I, Papernot N, Oliver A, Raffel CA (2019) Mixmatch: a holistic approach to semi-supervised learning. Adv Neural Inform Process Syst 32:5049\u20135059","journal-title":"Adv Neural Inform Process Syst"},{"key":"8269_CR46","first-page":"596","volume":"33","author":"K Sohn","year":"2020","unstructured":"Sohn K, Berthelot D, Carlini N, Zhang Z, Zhang H, Raffel CA, Cubuk ED, Kurakin A, Li C-L (2020) Fixmatch: simplifying semi-supervised learning with consistency and confidence. Adv Neural Inform Process Syst 33:596\u2013608","journal-title":"Adv Neural Inform Process Syst"},{"key":"8269_CR47","unstructured":"Tzeng E, Hoffman J, Zhang N, Saenko K, Darrell T (2014) Deep domain confusion: maximizing for domain invariance, arXiv preprint arXiv:1412.3474"},{"key":"8269_CR48","unstructured":"Long M, Cao Y, Wang J, Jordan M (2015) Learning transferable features with deep adaptation networks. In: Proceedings of the international conference on machine learning, pp 97\u2013105"},{"key":"8269_CR49","unstructured":"Zellinger W, Grubinger T, Lughofer E, Natschl\u00e4ger T, Saminger-Platz S (2017) Central moment discrepancy (cmd) for domain-invariant representation learning. In: Proceedings of the 5th international conference on learning representations"},{"issue":"8","key":"8269_CR50","doi-asserted-by":"publisher","first-page":"3545","DOI":"10.1007\/s00521-020-05228-4","volume":"33","author":"J Li","year":"2021","unstructured":"Li J, Li Z, L\u00fc S (2021) Unsupervised double weighted domain adaptation. Neural Comput Appl 33(8):3545\u20133566","journal-title":"Neural Comput Appl"},{"key":"8269_CR51","first-page":"1647","volume":"31","author":"M Long","year":"2018","unstructured":"Long M, Cao Z, Wang J, Jordan MI (2018) Conditional adversarial domain adaptation. Adv Neural Inform Process Syst 31:1647\u20131657","journal-title":"Adv Neural Inform Process Syst"},{"key":"8269_CR52","doi-asserted-by":"publisher","first-page":"106606","DOI":"10.1016\/j.knosys.2020.106606","volume":"212","author":"Q Zhou","year":"2021","unstructured":"Zhou Q, Wang S, Xing Y et al (2021) Multiple adversarial networks for unsupervised domain adaptation. Knowl-Based Syst 212:106606","journal-title":"Knowl-Based Syst"},{"key":"8269_CR53","doi-asserted-by":"crossref","unstructured":"Zhang W, Ouyang W, Li W, Xu D (2018) Collaborative and adversarial network for unsupervised domain adaptation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3801\u20133809","DOI":"10.1109\/CVPR.2018.00400"},{"key":"8269_CR54","doi-asserted-by":"crossref","unstructured":"Chen C, Xie W, Huang W, Rong Y, Ding X, Huang Y, Xu T, Huang J (2019) Progressive feature alignment for unsupervised domain adaptation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 627\u2013636","DOI":"10.1109\/CVPR.2019.00072"},{"key":"8269_CR55","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 248\u2013255","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"8269_CR56","doi-asserted-by":"crossref","unstructured":"Na J, Jung H, Chang HJ, Hwang W (2021) Fixbi: bridging domain spaces for unsupervised domain adaptation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 1094\u20131103","DOI":"10.1109\/CVPR46437.2021.00115"},{"key":"8269_CR57","doi-asserted-by":"crossref","unstructured":"Saenko K, Kulis B, Fritz M, Darrell T (2010) Adapting visual category models to new domains. In: Proceedings of the European conference on computer vision, pp 213\u2013226","DOI":"10.1007\/978-3-642-15561-1_16"},{"key":"8269_CR58","doi-asserted-by":"crossref","unstructured":"Venkateswara H, Eusebio J, Chakraborty S, Panchanathan S (2017) Deep hashing network for unsupervised domain adaptation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5018\u20135027","DOI":"10.1109\/CVPR.2017.572"},{"key":"8269_CR59","doi-asserted-by":"crossref","unstructured":"Wu Y, Inkpen D, El-Roby A (2020) Dual mixup regularized learning for adversarial domain adaptation. In: Proceedings of the European conference on computer vision, pp 540\u2013555","DOI":"10.1007\/978-3-030-58526-6_32"},{"key":"8269_CR60","doi-asserted-by":"publisher","first-page":"106774","DOI":"10.1016\/j.knosys.2021.106774","volume":"215","author":"F You","year":"2021","unstructured":"You F, Su H, Li J, Zhu L, Lu K, Yang Y (2021) Learning a weighted classifier for conditional domain adaptation. Knowl-Based Syst 215:106774","journal-title":"Knowl-Based Syst"},{"key":"8269_CR61","doi-asserted-by":"crossref","unstructured":"Xu R, Li G, Yang J, Lin L (2019) Larger norm more transferable: an adaptive feature norm approach for unsupervised domain adaptation. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 1426\u20131435","DOI":"10.1109\/ICCV.2019.00151"},{"key":"8269_CR62","doi-asserted-by":"crossref","unstructured":"Du Z, Li J, Su H, Zhu L, Lu K (2021) Cross-domain gradient discrepancy minimization for unsupervised domain adaptation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 3937\u20133946","DOI":"10.1109\/CVPR46437.2021.00393"},{"key":"8269_CR63","doi-asserted-by":"crossref","unstructured":"Xiao N, Zhang L (2021) Dynamic weighted learning for unsupervised domain adaptation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 15242\u201315251","DOI":"10.1109\/CVPR46437.2021.01499"},{"key":"8269_CR64","doi-asserted-by":"crossref","unstructured":"Chang W-G, You T, Seo S, Kwak S, Han B (2019) Domain-specific batch normalization for unsupervised domain adaptation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 7354\u20137362","DOI":"10.1109\/CVPR.2019.00753"},{"key":"8269_CR65","doi-asserted-by":"crossref","unstructured":"Zhang Y, Tang H, Jia K, Tan M (2019) Domain-symmetric networks for adversarial domain adaptation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 5031\u20135040","DOI":"10.1109\/CVPR.2019.00517"},{"key":"8269_CR66","doi-asserted-by":"crossref","unstructured":"Sharma A, Kalluri T, Chandraker M (2021) Instance level affinity-based transfer for unsupervised domain adaptation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 5361\u20135371","DOI":"10.1109\/CVPR46437.2021.00532"},{"key":"8269_CR67","doi-asserted-by":"crossref","unstructured":"Hu L, Kan M, Shan S, Chen X (2020) Unsupervised domain adaptation with hierarchical gradient synchronization. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 4043\u20134052","DOI":"10.1109\/CVPR42600.2020.00410"},{"issue":"11","key":"8269_CR68","doi-asserted-by":"publisher","first-page":"3918","DOI":"10.1109\/TPAMI.2020.2991050","volume":"43","author":"J Li","year":"2020","unstructured":"Li J, Chen E, Ding Z, Zhu L, Lu K, Shen HT (2020) Maximum density divergence for domain adaptation. IEEE Trans Pattern Anal Mach Intell 43(11):3918\u20133930","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"8269_CR69","unstructured":"Liang J, Hu D, Feng J (2020) Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation. In: Proceedings of the international conference on machine learning, pp 6028\u20136039"},{"key":"8269_CR70","doi-asserted-by":"publisher","DOI":"10.1201\/9781003217374","volume-title":"Ratio of momentum diffusivity to thermal diffusivity: introduction, meta-analysis, and scrutinization","author":"IL Animasaun","year":"2022","unstructured":"Animasaun IL, Shah NA, Wakif A, Mahanthesh B, Sivaraj R, Koriko OK (2022) Ratio of momentum diffusivity to thermal diffusivity: introduction, meta-analysis, and scrutinization. CRC Press, Cambridge"},{"key":"8269_CR71","doi-asserted-by":"publisher","first-page":"106069","DOI":"10.1016\/j.icheatmasstransfer.2022.106069","volume":"135","author":"W Cao","year":"2022","unstructured":"Cao W, Animasaun I, Yook S-J, Oladipupo V, Ji X (2022) Simulation of the dynamics of colloidal mixture of water with various nanoparticles at different levels of partial slip: Ternary-hybrid nanofluid. Int Commun Heat Mass Transfer 135:106069","journal-title":"Int Commun Heat Mass Transfer"},{"key":"8269_CR72","first-page":"2579","volume":"9","author":"L Van Der Maaten","year":"2008","unstructured":"Van Der Maaten L, Hinton G (2008) Visualizing data using t-sne. J Mach Learn Res 9:2579\u20132625","journal-title":"J Mach Learn Res"},{"key":"8269_CR73","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"},{"key":"8269_CR74","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF international conference on computer vision (ICCV)","DOI":"10.1109\/ICCV48922.2021.00986"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08269-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-08269-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08269-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,25]],"date-time":"2023-04-25T00:19:54Z","timestamp":1682381994000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-08269-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,26]]},"references-count":74,"journal-issue":{"issue":"15","published-print":{"date-parts":[[2023,5]]}},"alternative-id":["8269"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-08269-7","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2023,1,26]]},"assertion":[{"value":"2 August 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 January 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 January 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":"All authors disclosed no relevant relationships.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}