{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T14:48:22Z","timestamp":1780066102429,"version":"3.54.0"},"reference-count":89,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2022,8,13]],"date-time":"2022-08-13T00:00:00Z","timestamp":1660348800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,8,13]],"date-time":"2022-08-13T00:00:00Z","timestamp":1660348800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2023,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Having a lot of labeled data is always a problem in machine learning issues. Even by collecting lots of data hardly, shift in data distribution might emerge because of differences in source and target domains. The shift would make the model to face with problems in test step. Therefore, the necessity of using domain adaptation emerges. There are three techniques in the field of domain adaptation namely discrepancy based, adversarial based and reconstruction based methods. For domain adaptation, adversarial learning approaches showed state-of-the-art performance. Although there are some comprehensive surveys about domain adaptation, we technically focus on adversarial based domain adaptation methods. We examine each proposed method in detail with respect to their structures and objective functions. The common aspect of proposed methods besides domain adaptation is considering the target labels are predicted as accurately as possible. It can be represented by some methods such as metric learning and multi-adversarial discriminators as are used in some of the papers. Also, we address the negative transfer issue for dissimilar distributions and propose the addition of clustering heuristics to the underlying structures for future research.<\/jats:p>","DOI":"10.1007\/s11063-022-10977-5","type":"journal-article","created":{"date-parts":[[2022,8,13]],"date-time":"2022-08-13T15:02:54Z","timestamp":1660402974000},"page":"2429-2469","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":73,"title":["A Survey on Adversarial Domain Adaptation"],"prefix":"10.1007","volume":"55","author":[{"given":"Mahta","family":"HassanPour Zonoozi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5702-2209","authenticated-orcid":false,"given":"Vahid","family":"Seydi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,8,13]]},"reference":[{"key":"10977_CR1","unstructured":"Bergamo A, Torresani L (2010) Exploiting weakly-labeled web images to improve object classification: a domain adaptation approach. In: Advances in neural information processing systems. 181\u2013189"},{"key":"10977_CR2","doi-asserted-by":"crossref","unstructured":"Bousmalis K, Silberman N., Dohan D, Erhan D,Krishnan D (2017) Unsupervised pixel-level domain adaptation with generative adversarial networks. In: Proceedings of the 30th IEEE conference on computing vision pattern recognition, CVPR 2017. vol. 2017, 95\u2013104","DOI":"10.1109\/CVPR.2017.18"},{"key":"10977_CR3","doi-asserted-by":"crossref","unstructured":"Carlucci FM, Porzi L, Caputo B, Ricci E, Bul\u2018o SR (2017) Autodial: automatic domain alignment layers. In: International conference on computer vision","DOI":"10.1109\/ICCV.2017.542"},{"key":"10977_CR4","doi-asserted-by":"crossref","unstructured":"Chen Ch, Chen Zh, Jiang B, Jin X (2019) Joint domain alignment and discriminative feature learning for unsupervised deep domain adaptation. In: AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v33i01.33013296"},{"key":"10977_CR5","doi-asserted-by":"crossref","unstructured":"Cicek S, Soatto S (2019) Unsupervised domain adaptation via regularized conditional alignment. In: 2019 IEEE\/CVF international conference on computer vision (ICCV)","DOI":"10.1109\/ICCV.2019.00150"},{"key":"10977_CR6","unstructured":"Coates A, Ng Andrew, Lee H (2011) An analysis of single-layer networks in unsupervised feature learning. In: Proceedings of the fourteenth international conference on artificial intelligence and statistics, 215\u2013223"},{"key":"10977_CR7","unstructured":"Denker JS, Gardner WR, Graf HP, Henderson D, Howard RE, Hubbard W, Jackel LD, Baird HS, Guyon I (1989) Advances in neural information processing systems. 1. chapter Neural Network Recognizer for Handwritten Zip Code Digits. 323-331"},{"issue":"6","key":"10977_CR8","doi-asserted-by":"publisher","first-page":"1768","DOI":"10.1109\/TNNLS.2018.2874567","volume":"30","author":"Z Ding","year":"2018","unstructured":"Ding Z, Fu Y (2018) Deep transfer low-rank coding for cross-domain learning. IEEE Trans Neural Netw Learn Syst 30(6):1768\u20131779","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"10977_CR9","unstructured":"Donahue J, Jia Y, Vinyals O, Hoffman J, Zhang N, Tzeng E, Darrell T (2014) Decaf: A deep convolutional activation feature for generic visual recognition. In: International conference on machine learning (ICML). 647\u2013655"},{"key":"10977_CR10","unstructured":"Ganin Y, Lempitsky V (2015) Unsupervised domain adaptation by backpropagation. In: 32nd international conference on machine learning ICML 2015, vol 2, no. i 1180\u20131189"},{"issue":"9783319583464","key":"10977_CR11","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1007\/978-3-319-58347-1_10","volume":"17","author":"Y Ganin","year":"2017","unstructured":"Ganin Y, Ustinova E, Ajakan H, Germain P, Larochelle H, Laviolette F, Marchand M, Lempitsky V (2017) Domain-adversarial training of neural networks. Adv Comput Vis Pattern Recognit 17(9783319583464):189\u2013209","journal-title":"Adv Comput Vis Pattern Recognit"},{"key":"10977_CR12","doi-asserted-by":"crossref","unstructured":"Ghifary M, Kleijn WB, Zhang M, Balduzzi D, Li W (2016) Deep reconstruction classification networks for unsupervised domain adaptation. In: European conference on computer vision, 597\u2013613","DOI":"10.1007\/978-3-319-46493-0_36"},{"key":"10977_CR13","doi-asserted-by":"crossref","unstructured":"Long M, Cao Y, Cao Z, Wang J, Jordan MI (2018) Transferable representation learning with deep adaptation networks. In: IEEE transactions on pattern analysis and machine intelligence","DOI":"10.1109\/TPAMI.2018.2868685"},{"issue":"11","key":"10977_CR14","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2014","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Balduzzi D, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial networks. Commun ACM 63(11):139\u2013144. https:\/\/doi.org\/10.1145\/3422622","journal-title":"Commun ACM"},{"key":"10977_CR15","unstructured":"Gong B, Shi Y, Sha F, Grauman K (2012) Geodesic flow kernel for unsupervised domain adaptation. In: IEEE conference on computer vision and pattern recognition. 2066\u20132073"},{"key":"10977_CR16","unstructured":"Grandvalet Y, Bengio (2005) Semi-supervised learning by entropy minimization. In: Saul LK, Weiss Y, Bottou L (eds) Advances in neural information processing systems 17. MIT Press, 529\u2013536"},{"key":"10977_CR17","first-page":"131","volume-title":"Covariate shift and local learning by distribution matching","author":"A Gretton","year":"2009","unstructured":"Gretton A, Smola AJ, Huang J, Schmittfull M, Borgwardt KM, Sch\u00f6lkopf B (2009) Covariate shift and local learning by distribution matching. MIT Press, Cambridge, MA, pp 131\u2013160"},{"key":"10977_CR18","doi-asserted-by":"crossref","unstructured":"Gupta S, Girshick R, aez PA, Malik J (2014) Learning rich features from rgb-d images for object detection and segmentation. In: European conference on computer vision (ECCV)","DOI":"10.1007\/978-3-319-10584-0_23"},{"key":"10977_CR19","doi-asserted-by":"crossref","unstructured":"Han EHS, Karypis G, Kumar V (2001) Text categorization using weight adjusted k-nearest neighbor classification. In: PAKDD","DOI":"10.1007\/3-540-45357-1_9"},{"key":"10977_CR20","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Computer vision and pattern recognition","DOI":"10.1109\/CVPR.2016.90"},{"key":"10977_CR21","doi-asserted-by":"crossref","unstructured":"Hoffer E, Ailon N (2015) Deep metric learning using triplet network. In: International workshop on similarity-based pattern recognition","DOI":"10.1007\/978-3-319-24261-3_7"},{"key":"10977_CR22","doi-asserted-by":"crossref","unstructured":"Hu L, Kan M., Shan Sh, Chen X (2018) Duplex generative adversarial network for unsupervised domain adaptation. In: 2018 IEEE\/CVF conference on computer vision and pattern recognition, pp 1498\u20131507","DOI":"10.1109\/CVPR.2018.00162"},{"key":"10977_CR23","doi-asserted-by":"crossref","unstructured":"Isola P, Zhu JY, Zhou T, Efros A (2017) Image-to-image translation with conditional adversarial networks. In: Proceedings of the 30th IEEE conference on computing vision pattern recognition, CVPR. Vol. 2017, 5967\u20135976","DOI":"10.1109\/CVPR.2017.632"},{"issue":"2","key":"10977_CR24","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1109\/TPAMI.2017.2670560","volume":"40","author":"Y Jiang","year":"2018","unstructured":"Jiang Y, Wu Z, Wang J, Xue X, Chang S (2018) Exploiting feature and class relationships in video categorization with regularized deep neural networks. IEEE Trans Pattern Anal Mach Intell 40(2):352\u2013364","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10977_CR25","doi-asserted-by":"crossref","unstructured":"Klare B (2012) Towards automated caricature recognition. In: 2012 5th IAPR international conference on biometrics (ICB). 139\u2013146","DOI":"10.1109\/ICB.2012.6199771"},{"key":"10977_CR26","unstructured":"Krizhevsky A, Hinton G (2009) Learning multiple layers of features from tiny images. University of Toronto"},{"key":"10977_CR27","unstructured":"Kumar A, Sattigeri P, Wadhawan K, Karlinsky L, Feris RS, Freeman B, Wornell GW (2018) Co-regularized alignment for unsupervised domain adaptation, NeurIPS"},{"key":"10977_CR28","doi-asserted-by":"crossref","unstructured":"Kurmi V. K., and Namboodiri V. P. Looking back at labels: a class based domain adaptation technique. In: 2019 international joint conference on neural networks (IJCNN), pp 1\u20138 (2019)","DOI":"10.1109\/IJCNN.2019.8852199"},{"key":"10977_CR29","doi-asserted-by":"crossref","unstructured":"Laradji IH, Babanezhad R (2018) M-ADDA: unsupervised domain adaptation with deep metric learning. Domain Adapt Vis Underst 17\u201331","DOI":"10.1007\/978-3-030-30671-7_2"},{"issue":"11","key":"10977_CR30","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":"10977_CR31","doi-asserted-by":"crossref","unstructured":"Li S, Yi D, Lei Z, Liao S (2013) The CASIA NIR-VIS 2.0 face database. In: Computer vision and pattern recognition workshops","DOI":"10.1109\/CVPRW.2013.59"},{"key":"10977_CR32","unstructured":"Liu MY, Tuzel O (2016) Coupled generative adversarial networks. Adv Neural Inf Process Syst No. Nips, 469\u2013477"},{"key":"10977_CR33","unstructured":"Long M, Zhu H, Wang J, Jordan MI (2016) Unsupervised domain adaptation with residual transfer networks. Adv Neural Inf Process Syst Nips. 136\u2013144"},{"key":"10977_CR34","unstructured":"Long M, Cao Y, Wang J, Jordan M (2015) Learning transferable features with deep adaptation networks. In: International conference on machine learning, pp 97\u201310"},{"key":"10977_CR35","unstructured":"Long M, Cao Z, Wang J, Jordan MI (2018) Conditional adversarial domain adaptation. In: Advances in neural information processing systems, pp 1645\u20131655"},{"key":"10977_CR36","doi-asserted-by":"crossref","unstructured":"Long M, Wang J, Ding G, Sun J, Philip SY (2013) Transfer feature learning with joint distribution adaptation. In: 2013 IEEE international conference on computer vision, pp 2200\u20132207","DOI":"10.1109\/ICCV.2013.274"},{"key":"10977_CR37","unstructured":"Long M, Zhu H., Wang J, Jordan M (2017) Deep transfer learning with joint adaptation networks. In: International conference on machine learning. JMLR. org. 2208-2217"},{"key":"10977_CR38","unstructured":"Lucic M, Kurach M, Michalski M, Bousquet O, Gelly S (2018) Are Gans created equal? A large-scale study. Adv Neural Inf Process Syst vol. 2018, pp 597\u2013613"},{"issue":"19","key":"10977_CR39","doi-asserted-by":"publisher","first-page":"3283","DOI":"10.1049\/iet-ipr.2020.0087","volume":"14","author":"Y Madadi","year":"2020","unstructured":"Madadi Y, Seydi V, Nasrollahi K, Hosseini R, Moeslund T (2020) Deep visual unsupervised domain adaptation for classification tasks: a survey. IET Image Proc 14(19):3283\u20133299","journal-title":"IET Image Proc"},{"key":"10977_CR40","doi-asserted-by":"crossref","unstructured":"Mao X, Li Q, Xie H, Lau RYK, Wang Z (2016) Multiclass generative adversarial networks with the L2 loss function. CoRR, arXiv:1611.04076","DOI":"10.1109\/ICCV.2017.304"},{"key":"10977_CR41","unstructured":"Mirza M, and Osindero S (2014) Conditional generative adversarial nets. CoRR, arXiv:1411.1784"},{"key":"10977_CR42","doi-asserted-by":"crossref","unstructured":"Mittal P, Jain A, Goswami G, Singh R, Vatsa M (2014) Recognizing composite sketches with digital face images via ssd dictionary. In: International joint conference on biometrics","DOI":"10.1109\/BTAS.2014.6996265"},{"key":"10977_CR43","unstructured":"Miyato T, Maeda S, Koyama M, Nakae K, Ishii S (2015) Distributional smoothing with virtual adversarial training. arXiv preprint arXiv:1507.00677"},{"key":"10977_CR44","doi-asserted-by":"publisher","first-page":"1979","DOI":"10.1109\/TPAMI.2018.2858821","volume":"41","author":"T Miyato","year":"2017","unstructured":"Miyato T, Maeda S, Koyama M, Ishii S (2017) Virtual adversarial training: a regularization method for supervised and semi-supervised learning. IEEE Trans Pattern Anal Mach Intell 41:1979\u20131993","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10977_CR45","doi-asserted-by":"crossref","unstructured":"Murez Z, Kolouri S, Kriegman D, Ramamoorthi R, Kim K (2018) Image to image translation for domain adaptation. In: IEEE conference on computer vision and pattern recognition, pp 4500\u20134509","DOI":"10.1109\/CVPR.2018.00473"},{"key":"10977_CR46","unstructured":"Netzer Y, Wang T, Coates A, Bissacco A, Wu B, Ng AY (2011) Reading digits in natural images with unsupervised feature learning, In: NIPS workshop on deep learning and unsupervised feature learning"},{"issue":"10","key":"10977_CR47","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345\u20131359","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"2","key":"10977_CR48","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1109\/TNN.2010.2091281","volume":"22","author":"SJ Pan","year":"2011","unstructured":"Pan SJ, Tsang TW, Kwok JT, Yang Q (2011) Domain adaptation via transfer component analysis. IEEE Trans Neural Netw 22(2):199\u2013210","journal-title":"IEEE Trans Neural Netw"},{"issue":"4","key":"10977_CR49","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1023\/A:1008981510081","volume":"10","author":"D Peel","year":"2000","unstructured":"Peel D, McLachlan GJ (2000) Robust mixture modelling using the t distribution. Stat Comput 10(4):339\u2013348","journal-title":"Stat Comput"},{"key":"10977_CR50","doi-asserted-by":"crossref","unstructured":"Pei Z, Cao Z, Long M, Wang J (2018) Multi-adversarial domain adaptation. In: 32nd AAAI conference on artificial intelligence AAAI 2018, pp 3934-394","DOI":"10.1609\/aaai.v32i1.11767"},{"key":"10977_CR51","unstructured":"Peng X, Usman B, Kaushik N, Hoffman J, Wang D, Saenko K (2017) VisDA: The visual domain adaptation challenge. arXiv:1710.06924"},{"key":"10977_CR52","doi-asserted-by":"crossref","unstructured":"Peng X, Usman B, Saito K, Kaushik N, Hoffman J, Saenko K (2018) Syn2real: A new benchmark for synthetic-to-real visual domain adaptation. arXiv:1806.09755","DOI":"10.1109\/CVPRW.2018.00271"},{"key":"10977_CR53","doi-asserted-by":"publisher","unstructured":"Peng KC, Wu Z, Ernst J (2018) Zero-shot deep domain adaptation. Lect Notes Comput Sci (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics). Vol. 11215 LNCS, 793-810. https:\/\/doi.org\/10.1007\/978-3-030-01252-6_47","DOI":"10.1007\/978-3-030-01252-6_47"},{"key":"10977_CR54","doi-asserted-by":"crossref","unstructured":"Rozantsev A, Salzmann M, Fua P (2018) Beyond sharing weights for deep domain adaptation. IEEE Trans Pattern Anal Mach Intell","DOI":"10.1109\/TPAMI.2018.2814042"},{"key":"10977_CR55","doi-asserted-by":"crossref","unstructured":"Rumelhart DE, Hinton GE, Williams RJ (1986) Parallel distributed processing: explorations in the microstructure of cognition. Chapter Learning Internal Representations by Error Propagation. MIT Press, Cambridge, MA, USA, pp 318\u2013362","DOI":"10.7551\/mitpress\/5236.001.0001"},{"key":"10977_CR56","doi-asserted-by":"crossref","unstructured":"Saenko K, Kulis B, Fritz M, Darrell T (2010) Adapting visual category models to new domains. In: European conference on computer vision. Springer, pp 213\u201322","DOI":"10.1007\/978-3-642-15561-1_16"},{"key":"10977_CR57","unstructured":"Saito K, Ushiku Y, Harada T (2017) Asymmetric tri-training for unsupervised domain adaptation. arXiv preprint arXiv:1702.08400"},{"key":"10977_CR58","doi-asserted-by":"crossref","unstructured":"Saito K, Watanabe K, Ushiku Y, Harada T (2017) Maximum classifier discrepancy for unsupervised domain adaptation. In: IEEE\/CVF conference on computer vision and pattern recognition, 3723\u20133732","DOI":"10.1109\/CVPR.2018.00392"},{"key":"10977_CR59","doi-asserted-by":"crossref","unstructured":"Sankaranarayanan S, Balaji Y, Castillo CD, Chellappa R (2018) Generate to adapt: aligning domains using generative adversarial networks. In: Computer vision and pattern recognition","DOI":"10.1109\/CVPR.2018.00887"},{"key":"10977_CR60","doi-asserted-by":"crossref","unstructured":"Schroff F, Kalenichenko D, Philbin J (2015) FaceNet: A unified embedding for face recognition and clustering. In: Proceedings of the IEEE conference on computer vision and pattern recognition. vol 07-12-June, pp 815\u2013823","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"10977_CR61","unstructured":"Sener O, Song HO, Saxena A, Savarese S (2016) Learning transferrable representations for unsupervised domain adaptation. In: Advances in neural information processing systems, pp 2110-2118"},{"key":"10977_CR62","unstructured":"Shu R, Bui HH, Narui H, Ermon S (2018) A DIrt-t approach to unsupervised domain adaptation. In: 6th international conference on learning representation. ICLR 2018 - Conf. Track Proc. 1-19"},{"key":"10977_CR63","doi-asserted-by":"crossref","unstructured":"Silberman N, Hoiem D, Kohli P, Fergus R (2012) Indoor segmentation and support inference from rgbd images. In: European conference on computer vision (ECCV)","DOI":"10.1007\/978-3-642-33715-4_54"},{"key":"10977_CR64","unstructured":"Sun Y, Chen Y, Wang X, Tang X (2014) Deep learning face representation by joint identification-verification. In: Advances in neural information processing systems, pp. 1988\u20131996"},{"key":"10977_CR65","doi-asserted-by":"crossref","unstructured":"Sun B, Saenko K (2016) Deep coral: correlation alignment for deep domain adaptation. In: ECCV. Springer, Berlin, 443\u2013450","DOI":"10.1007\/978-3-319-49409-8_35"},{"key":"10977_CR66","doi-asserted-by":"crossref","unstructured":"Tang H, Jia J (2020) Discriminative adversarial domain adaptation. In: AAAI 2020 - 34th AAAI conference on artifical intelligence, pp 5940\u20135947","DOI":"10.1609\/aaai.v34i04.6054"},{"key":"10977_CR67","doi-asserted-by":"crossref","unstructured":"Tommasi T, Tuytelaars T (2014) A testbed for cross-dataset analysis. In: ECCV workshop on transferring and adapting source knowledge in computer vision (TASK-CV)","DOI":"10.1007\/978-3-319-16199-0_2"},{"key":"10977_CR68","doi-asserted-by":"crossref","unstructured":"Torralba A, Efros A (2011) Unbiased look at dataset bias. In: CVPR\u201911 (2011)","DOI":"10.1109\/CVPR.2011.5995347"},{"key":"10977_CR69","doi-asserted-by":"crossref","unstructured":"Tran L, Sohn K, Yu X, Liu X, Chandraker MK (2019) Gotta adapt \u2019em all: Joint pixel and feature-level domain adaptation for recognition in the wild. In: Computer vision and pattern recognition","DOI":"10.1109\/CVPR.2019.00278"},{"key":"10977_CR70","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":"10977_CR71","doi-asserted-by":"publisher","unstructured":"Tzeng E, Hoffman J, Saenko K, Darrell T (2017) Adversarial discriminative domain adaptation. In: Proceedings of the 30th IEEE conference on computer vision pattern recognition, CVPR 2017. Vol. 2017, pp 2962\u20132971. https:\/\/doi.org\/10.1109\/CVPR.2017.316","DOI":"10.1109\/CVPR.2017.316"},{"key":"10977_CR72","doi-asserted-by":"publisher","unstructured":"Tzeng E, Devin C., Hoffman J, Finn C, Abbeel P, Levine S, Seanko K Darrell T (2020) Adapting deep visuomotor representations with weak pairwise constraints. Published in WAFR 2016 Computer Science. 688-703. https:\/\/doi.org\/10.1007\/978-3-030-43089-4_44","DOI":"10.1007\/978-3-030-43089-4_44"},{"key":"10977_CR73","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 CVPR, pp. 5018\u20135027","DOI":"10.1109\/CVPR.2017.572"},{"key":"10977_CR74","first-page":"3371","volume":"11","author":"P Vincent","year":"2010","unstructured":"Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol PA (2010) Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11:3371\u20133408","journal-title":"J Mach Learn Res"},{"key":"10977_CR75","doi-asserted-by":"publisher","unstructured":"Volpi R, Morerio P, Savarese S, Murino V (2018) Adversarial feature augmentation for unsupervised domain adaptation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5495\u20135504. https:\/\/doi.org\/10.1109\/CVPR.2018.00576","DOI":"10.1109\/CVPR.2018.00576"},{"issue":"11","key":"10977_CR76","doi-asserted-by":"publisher","first-page":"1955","DOI":"10.1109\/TPAMI.2008.222","volume":"31","author":"X Wang","year":"2009","unstructured":"Wang X, Tang X (2009) Face photo-sketch synthesis and recognition. TPAMI 31(11):1955\u20131967","journal-title":"TPAMI"},{"key":"10977_CR77","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.neucom.2018.05.083","volume":"312","author":"M Wang","year":"2018","unstructured":"Wang M, Deng W (2018) Deep visual domain adaptation: a survey. Neurocomputing 312:135\u2013153. https:\/\/doi.org\/10.1016\/j.neucom.2018.05.083","journal-title":"Neurocomputing"},{"key":"10977_CR78","unstructured":"Wang R, Wang G, Henao R (2019) Discriminative clustering for robust unsupervised domain adaptation. arxiv"},{"key":"10977_CR79","unstructured":"Wang Z, Jing B, Ni Y, Dong N, Xie P, Xing EP (2020) Adversarial domain adaptation being aware of class relationships. In: ECAI"},{"key":"10977_CR80","unstructured":"Weinberger KQ, Saul LK (2009) Distance metric learning for large margin nearest neighbor classification. In: JMLR"},{"key":"10977_CR81","doi-asserted-by":"crossref","unstructured":"Wen Y, Zhang K, Zhang MLBZ, Qiao Y (2016) A discriminative feature learning approach. In: Eccv, pp 499\u2013515","DOI":"10.1007\/978-3-319-46478-7_31"},{"key":"10977_CR82","unstructured":"Wilson G, Cook DJ (2018) A survey of unsupervised deep domain adaptation. arXiv"},{"key":"10977_CR83","volume-title":"Distance metric learning with application to clustering with side-information","author":"Eric P Xing","year":"2003","unstructured":"Xing Eric P, Zhang M, Ng Andrew Y, Jordan M, Russell S (2003) Distance metric learning with application to clustering with side-information. MIT Press, Cambridge, MA"},{"key":"10977_CR84","unstructured":"Yuntao D, Zhiwen T., Qian Ch, Xiaowen Z, Yirong Y, Chongjun W (2020) Dual adversarial domain adaptation. arXiv preprint arXiv:2001.00153"},{"key":"10977_CR85","doi-asserted-by":"crossref","unstructured":"Zhang W, Wang X, and Tang X (2011) Coupled information theoretic encoding for face photo-sketch recognition. In: 2011 IEEE CVPR, pp 513\u2013520. IEEE","DOI":"10.1109\/CVPR.2011.5995324"},{"key":"10977_CR86","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":"10977_CR87","unstructured":"Zhang Y, Wang Y, Tian Q (2018) Domain-invariant adversarial learning for unsupervised domain adaption arXiv"},{"key":"10977_CR88","unstructured":"Zhao H (2017) Domain adaptation with adversarial neural networks and auto-encoders"},{"key":"10977_CR89","unstructured":"Zhuang F, Cheng X, Luo P, Pan SJ, He Q (2015) Supervised representation learning: transfer learning with deep autoencoders. In: IJCAI, pp 4119-4125"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-022-10977-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-022-10977-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-022-10977-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T17:38:45Z","timestamp":1727804325000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-022-10977-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,13]]},"references-count":89,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["10977"],"URL":"https:\/\/doi.org\/10.1007\/s11063-022-10977-5","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"value":"1370-4621","type":"print"},{"value":"1573-773X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,13]]},"assertion":[{"value":"20 July 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 August 2022","order":2,"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":"Conflicts of interest"}}]}}