{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T10:20:36Z","timestamp":1777890036355,"version":"3.51.4"},"reference-count":23,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2020,11,1]],"date-time":"2020-11-01T00:00:00Z","timestamp":1604188800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Web Intelligence"],"published-print":{"date-parts":[[2020,11]]},"abstract":"<jats:p>Domain adaptation aims to solve the problems of lacking labels. Most existing\n                    works of domain adaptation mainly focus on aligning the feature distributions\n                    between the source and target domain. However, in the field of Natural Language\n                    Processing, some of the words in different domains convey different sentiment.\n                    Thus not all features of the source domain should be transferred, and it would\n                    cause negative transfer when aligning the untransferable features. To address\n                    this issue, we propose a Correlation Alignment with Attention mechanism for\n                    unsupervised Domain Adaptation (CAADA) model. In the model, an attention\n                    mechanism is introduced into the transfer process for domain adaptation, which\n                    can capture the positively transferable features in source and target domain.\n                    Moreover, the CORrelation ALignment (CORAL) loss is utilized to minimize the\n                    domain discrepancy by aligning the second-order statistics of the positively\n                    transferable features extracted by the attention mechanism. Extensive\n                    experiments on the Amazon review dataset demonstrate the effectiveness of CAADA\n                    method.<\/jats:p>","DOI":"10.3233\/web-210447","type":"journal-article","created":{"date-parts":[[2021,5,7]],"date-time":"2021-05-07T14:55:06Z","timestamp":1620399306000},"page":"261-267","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Correlation alignment with attention mechanism for unsupervised                    domain adaptation"],"prefix":"10.1177","volume":"18","author":[{"given":"Rong","family":"Chen","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Shandong University of Technology, China.\r                        E-mail:\u00a0"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chongguang","family":"Ren","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Shandong University of Technology, China.\r                        E-mail:\u00a0"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2021,5,6]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","unstructured":"D.\u00a0Tang B.\u00a0Qin and T.\u00a0Liu Document modeling with gated recurrent neural network for sentiment classification in: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing 2015 pp.\u00a01422\u20131432. doi:10.18653\/v1\/D15-1167.","DOI":"10.18653\/v1\/D15-1167"},{"key":"e_1_3_2_3_2","doi-asserted-by":"crossref","unstructured":"Z.\u00a0Yang D.\u00a0Yang C.\u00a0Dyer X.\u00a0He A.\u00a0Smola and E.\u00a0Hovy Hierarchical attention networks for document classification in: Proceedings of NAACL-HLT 2016 2016 pp.\u00a01480\u20131489.","DOI":"10.18653\/v1\/N16-1174"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","unstructured":"Y.\u00a0Feng Z.\u00a0Zhang X.\u00a0Zhao R.\u00a0Ji and 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E.\u00a0Ustinova, H.\u00a0Ajakan, P.\u00a0Germain, H.\u00a0Larochelle, F.\u00a0Laviolette, M.\u00a0Marchand and V.\u00a0Lempitsky, Domain-adversarial training of neural networks, The Journal of Machine Learning Research 17(1) (2016), 2096\u20133030.","journal-title":"The Journal of Machine Learning Research"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","unstructured":"J.\u00a0Zhuo S.\u00a0Wang W.\u00a0Zhang and Q.\u00a0Huang Deep unsupervised convolutional domain adaptation in: Proceedings of the Twenty-Fifth ACM International Conference on Multimedia 2017 pp.\u00a0261\u2013269. doi:10.1145\/3123266.3123292.","DOI":"10.1145\/3123266.3123292"},{"key":"e_1_3_2_11_2","unstructured":"E.\u00a0Tzeng J.\u00a0Hoffman N.\u00a0Zhang K.\u00a0Saenko and T.\u00a0Darrell Deep domain confusion: Maximizing for domain invariance 2014 arXiv preprint arXiv:1412.3474."},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2016.2554549"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","unstructured":"Q.\u00a0Li X.\u00a0Zhang J.\u00a0Xiong W.M.\u00a0Hwu and D.\u00a0Chen Implementing neural machine translation with bi-directional GRU and attention mechanism on FPGAs using HLS in: Proceedings of the Twenty-Fourth Asia and South Pacific Design Automation Conference 2019 pp.\u00a0693\u2013698. doi:10.1145\/3287624.3287717.","DOI":"10.1145\/3287624.3287717"},{"key":"e_1_3_2_14_2","doi-asserted-by":"crossref","unstructured":"C.\u00a0Kaul S.\u00a0Manandhar and N.\u00a0Pears FocusNet: An attention-based Fully Convolutional Network for Medical Image Segmentation 2019 arXiv preprint arXiv:1902.03091.","DOI":"10.1109\/ISBI.2019.8759477"},{"key":"e_1_3_2_15_2","doi-asserted-by":"crossref","unstructured":"X.\u00a0Wang L.\u00a0Li W.\u00a0Ye M.\u00a0Long and J.\u00a0Wang Transferable attention for domain adaptation in: AAAI 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