{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,11,7]],"date-time":"2023-11-07T00:47:24Z","timestamp":1699318044476},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2023,10,13]],"date-time":"2023-10-13T00:00:00Z","timestamp":1697155200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,13]],"date-time":"2023-10-13T00:00:00Z","timestamp":1697155200000},"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":["Machine Vision and Applications"],"published-print":{"date-parts":[[2023,11]]},"DOI":"10.1007\/s00138-023-01472-5","type":"journal-article","created":{"date-parts":[[2023,10,13]],"date-time":"2023-10-13T16:01:43Z","timestamp":1697212903000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Two-stage structural information enhancement for source-free domain adaptation"],"prefix":"10.1007","volume":"34","author":[{"given":"Sijie","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingwen","family":"Shao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lixu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyuan","family":"Bao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,13]]},"reference":[{"key":"1472_CR1","first-page":"1517","volume":"11","author":"BK Sriperumbudur","year":"2010","unstructured":"Sriperumbudur, B.K., Gretton, A., Fukumizu, K., Sch\u00f6lkopf, B., Lanckriet, G.R.: Hilbert space embeddings and metrics on probability measures. J. Mach. Learn. Res. 11, 1517\u20131561 (2010)","journal-title":"J. Mach. Learn. Res."},{"issue":"1","key":"1472_CR2","first-page":"723","volume":"13","author":"A Gretton","year":"2012","unstructured":"Gretton, A., Borgwardt, K.M., Rasch, M.J., Sch\u00f6lkopf, B., Smola, A.: A kernel two-sample test. J. Mach. Learn. Res. 13(1), 723\u2013773 (2012)","journal-title":"J. Mach. Learn. Res."},{"key":"1472_CR3","doi-asserted-by":"crossref","unstructured":"Redko, I., Morvant, E., Habrard, A., Sebban, M., Bennani, Y.: A survey on domain adaptation theory: learning bounds and theoretical guarantees. arXiv preprint arXiv:2004.11829 (2020)","DOI":"10.1016\/B978-1-78548-236-6.50002-7"},{"key":"1472_CR4","unstructured":"Tzeng, E., Hoffman, J., Zhang, N., Saenko, K., Darrell, T.: Deep domain confusion: maximizing for domain invariance. arXiv preprint arXiv:1412.3474 (2014)"},{"key":"1472_CR5","unstructured":"Long, M., Cao, Y., Wang, J., Jordan, M.: Learning transferable features with deep adaptation networks. In: International Conference on Machine Learning, pp. 97\u2013105 (2015). PMLR"},{"issue":"12","key":"1472_CR6","doi-asserted-by":"publisher","first-page":"3071","DOI":"10.1109\/TPAMI.2018.2868685","volume":"41","author":"M Long","year":"2018","unstructured":"Long, M., Cao, Y., Cao, Z., Wang, J., Jordan, M.I.: Transferable representation learning with deep adaptation networks. IEEE Trans. Pattern Anal. Mach. Intell. 41(12), 3071\u20133085 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1472_CR7","unstructured":"Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. In: International Conference on Machine Learning, pp. 1180\u20131189 (2015). PMLR"},{"issue":"1","key":"1472_CR8","first-page":"2096","volume":"17","author":"Y Ganin","year":"2016","unstructured":"Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., Lempitsky, V.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(1), 2096 (2016)","journal-title":"J. Mach. Learn. Res."},{"key":"1472_CR9","doi-asserted-by":"crossref","unstructured":"Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: Adversarial discriminative domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7167\u20137176 (2017)","DOI":"10.1109\/CVPR.2017.316"},{"key":"1472_CR10","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tang, H., Jia, K., Tan, M.: Domain-symmetric networks for adversarial domain adaptation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5031\u20135040 (2019)","DOI":"10.1109\/CVPR.2019.00517"},{"key":"1472_CR11","first-page":"29393","volume":"34","author":"S Yang","year":"2021","unstructured":"Yang, S., Weijer, J., Herranz, L., Jui, S., et al.: Exploiting the intrinsic neighborhood structure for source-free domain adaptation. Adv. Neural. Inf. Process. Syst. 34, 29393\u201329405 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"1472_CR12","doi-asserted-by":"crossref","unstructured":"Yang, S., Wang, Y., Weijer, J., Herranz, L., Jui, S.: Generalized source-free domain adaptation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 8978\u20138987 (2021)","DOI":"10.1109\/ICCV48922.2021.00885"},{"key":"1472_CR13","doi-asserted-by":"crossref","unstructured":"Li, R., Jiao, Q., Cao, W., Wong, H.S., Wu, S.: Model adaptation: unsupervised domain adaptation without source data. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9641\u20139650 (2020)","DOI":"10.1109\/CVPR42600.2020.00966"},{"key":"1472_CR14","doi-asserted-by":"crossref","unstructured":"Xia, H., Zhao, H., Ding, Z.: Adaptive adversarial network for source-free domain adaptation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9010\u20139019 (2021)","DOI":"10.1109\/ICCV48922.2021.00888"},{"key":"1472_CR15","doi-asserted-by":"crossref","unstructured":"Saenko, K., Kulis, B., Fritz, M., Darrell, T.: Adapting visual category models to new domains. In: European Conference on Computer Vision, pp. 213\u2013226 (2010). Springer","DOI":"10.1007\/978-3-642-15561-1_16"},{"key":"1472_CR16","doi-asserted-by":"crossref","unstructured":"Venkateswara, H., Eusebio, J., Chakraborty, S., Panchanathan, S.: Deep hashing network for unsupervised domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5018\u20135027 (2017)","DOI":"10.1109\/CVPR.2017.572"},{"key":"1472_CR17","unstructured":"Peng, X., Usman, B., Kaushik, N., Hoffman, J., Wang, D., Saenko, K.: Visda: The visual domain adaptation challenge. arXiv preprint arXiv:1710.06924 (2017)"},{"key":"1472_CR18","unstructured":"Kundu, J.N., Kulkarni, A.R., Bhambri, S., Mehta, D., Kulkarni, S.A., Jampani, V., Radhakrishnan, V.B.: Balancing discriminability and transferability for source-free domain adaptation. In: International Conference on Machine Learning, pp. 11710\u201311728 (2022). PMLR"},{"issue":"11","key":"1472_CR19","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial networks. Commun. ACM 63(11), 139\u2013144 (2020)","journal-title":"Commun. ACM"},{"key":"1472_CR20","unstructured":"Long, M., Cao, Z., Wang, J., Jordan, M.I.: Conditional adversarial domain adaptation. Advances in neural information processing systems 31 (2018)"},{"key":"1472_CR21","doi-asserted-by":"crossref","unstructured":"Saito, K., Watanabe, K., Ushiku, Y., Harada, T.: Maximum classifier discrepancy for unsupervised domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3723\u20133732 (2018)","DOI":"10.1109\/CVPR.2018.00392"},{"key":"1472_CR22","unstructured":"Zhang, Y., Liu, T., Long, M., Jordan, M.: Bridging theory and algorithm for domain adaptation. In: International Conference on Machine Learning, pp. 7404\u20137413 (2019). PMLR"},{"key":"1472_CR23","unstructured":"Liang, J., Hu, D., Feng, J.: Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation. In: International Conference on Machine Learning, pp. 6028\u20136039 (2020). PMLR"},{"key":"1472_CR24","doi-asserted-by":"crossref","unstructured":"Liang, J., Hu, D., Wang, Y., He, R., Feng, J.: Source data-absent unsupervised domain adaptation through hypothesis transfer and labeling transfer. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021)","DOI":"10.1109\/TPAMI.2021.3103390"},{"key":"1472_CR25","unstructured":"Yang, S., Wang, Y., Weijer, J., Herranz, L., Jui, S.: Unsupervised domain adaptation without source data by casting a bait. arXiv preprint arXiv:2010.12427 1(2), 5 (2020)"},{"key":"1472_CR26","first-page":"3635","volume":"34","author":"J Huang","year":"2021","unstructured":"Huang, J., Guan, D., Xiao, A., Lu, S.: Model adaptation: historical contrastive learning for unsupervised domain adaptation without source data. Adv. Neural Inform. Process. Syst. 34, 3635\u20133649 (2021)","journal-title":"Adv. Neural Inform. Process. Syst."},{"key":"1472_CR27","doi-asserted-by":"crossref","unstructured":"Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3733\u20133742 (2018)","DOI":"10.1109\/CVPR.2018.00393"},{"key":"1472_CR28","unstructured":"Yang, S., Wang, Y., Wang, K., Jui, S., et al.: Attracting and dispersing: A simple approach for source-free domain adaptation. In: Advances in Neural Information Processing Systems (2022)"},{"key":"1472_CR29","doi-asserted-by":"crossref","unstructured":"Wang, F., Han, Z., Gong, Y., Yin, Y.: Exploring domain-invariant parameters for source free domain adaptation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7151\u20137160 (2022)","DOI":"10.1109\/CVPR52688.2022.00701"},{"key":"1472_CR30","unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017)"},{"key":"1472_CR31","unstructured":"Kim, J.H., Choo, W., Jeong, H., Song, H.O.: Co-mixup: Saliency guided joint mixup with supermodular diversity. arXiv preprint arXiv:2102.03065 (2021)"},{"key":"1472_CR32","doi-asserted-by":"crossref","unstructured":"Chou, H.P., Chang, S.C., Pan, J.Y., Wei, W., Juan, D.C.: Remix: rebalanced mixup. In: European Conference on Computer Vision, pp. 95\u2013110 (2020). Springer","DOI":"10.1007\/978-3-030-65414-6_9"},{"key":"1472_CR33","doi-asserted-by":"crossref","unstructured":"Na, J., Jung, H., Chang, H.J., Hwang, W.: Fixbi: Bridging domain spaces for unsupervised domain adaptation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1094\u20131103 (2021)","DOI":"10.1109\/CVPR46437.2021.00115"},{"key":"1472_CR34","doi-asserted-by":"crossref","unstructured":"Wang, X., Li, L., Ye, W., Long, M., Wang, J.: Transferable attention for domain adaptation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 5345\u20135352 (2019)","DOI":"10.1609\/aaai.v33i01.33015345"},{"key":"1472_CR35","doi-asserted-by":"crossref","unstructured":"Cui, S., Wang, S., Zhuo, J., Su, C., Huang, Q., Tian, Q.: Gradually vanishing bridge for adversarial domain adaptation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12455\u201312464 (2020)","DOI":"10.1109\/CVPR42600.2020.01247"},{"key":"1472_CR36","doi-asserted-by":"crossref","unstructured":"Tang, H., Chen, K., Jia, K.: Unsupervised domain adaptation via structurally regularized deep clustering. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8725\u20138735 (2020)","DOI":"10.1109\/CVPR42600.2020.00875"},{"key":"1472_CR37","doi-asserted-by":"crossref","unstructured":"Lee, C.Y., Batra, T., Baig, M.H., Ulbricht, D.: Sliced wasserstein discrepancy for unsupervised domain adaptation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10285\u201310295 (2019)","DOI":"10.1109\/CVPR.2019.01053"},{"key":"1472_CR38","doi-asserted-by":"crossref","unstructured":"Jin, Y., Wang, X., Long, M., Wang, J.: Minimum class confusion for versatile domain adaptation. In: European Conference on Computer Vision, pp. 464\u2013480 (2020). Springer","DOI":"10.1007\/978-3-030-58589-1_28"},{"key":"1472_CR39","doi-asserted-by":"crossref","unstructured":"Lu, Z., Yang, Y., Zhu, X., Liu, C., Song, Y.Z., Xiang, T.: Stochastic classifiers for unsupervised domain adaptation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9111\u20139120 (2020)","DOI":"10.1109\/CVPR42600.2020.00913"},{"key":"1472_CR40","doi-asserted-by":"crossref","unstructured":"Xu, R., Liu, P., Wang, L., Chen, C., Wang, J.: Reliable weighted optimal transport for unsupervised domain adaptation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4394\u20134403 (2020)","DOI":"10.1109\/CVPR42600.2020.00445"},{"key":"1472_CR41","doi-asserted-by":"crossref","unstructured":"Xu, R., Li, G., Yang, J., Lin, L.: 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 (2019)","DOI":"10.1109\/ICCV.2019.00151"},{"key":"1472_CR42","doi-asserted-by":"crossref","unstructured":"Chang, W.G., You, T., Seo, S., Kwak, S., Han, B.: Domain-specific batch normalization for unsupervised domain adaptation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7354\u20137362 (2019)","DOI":"10.1109\/CVPR.2019.00753"},{"key":"1472_CR43","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"}],"container-title":["Machine Vision and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-023-01472-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00138-023-01472-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-023-01472-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,6]],"date-time":"2023-11-06T17:10:01Z","timestamp":1699290601000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00138-023-01472-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,13]]},"references-count":43,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2023,11]]}},"alternative-id":["1472"],"URL":"https:\/\/doi.org\/10.1007\/s00138-023-01472-5","relation":{},"ISSN":["0932-8092","1432-1769"],"issn-type":[{"value":"0932-8092","type":"print"},{"value":"1432-1769","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,13]]},"assertion":[{"value":"12 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 July 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 September 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 October 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"121"}}