{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T01:20:12Z","timestamp":1772932812828,"version":"3.50.1"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2023,12,20]],"date-time":"2023-12-20T00:00:00Z","timestamp":1703030400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,20]],"date-time":"2023-12-20T00:00:00Z","timestamp":1703030400000},"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":["No. 62192783"],"award-info":[{"award-number":["No. 62192783"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1811462"],"award-info":[{"award-number":["U1811462"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mach Learn"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1007\/s10994-023-06432-8","type":"journal-article","created":{"date-parts":[[2023,12,21]],"date-time":"2023-12-21T05:02:20Z","timestamp":1703134940000},"page":"3611-3631","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Generation, augmentation, and alignment: a pseudo-source domain based method for source-free domain adaptation"],"prefix":"10.1007","volume":"113","author":[{"given":"Yuntao","family":"Du","sequence":"first","affiliation":[]},{"given":"Haiyang","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Mingcai","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Hongtao","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Juan","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Yi","family":"Xin","sequence":"additional","affiliation":[]},{"given":"Chongjun","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,20]]},"reference":[{"key":"6432_CR1","doi-asserted-by":"crossref","unstructured":"Ahmed, S. M., Raychaudhuri, D. S., Paul, S., Oymak, S., & Roy-Chowdhury, A. (2021). Unsupervised multi-source domain adaptation without access to source data. In CVPR.","DOI":"10.1109\/CVPR46437.2021.00997"},{"key":"6432_CR2","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1007\/s10994-009-5152-4","volume":"79","author":"S Ben-David","year":"2009","unstructured":"Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F. C., & Vaughan, J. W. (2009). A theory of learning from different domains. Machine Learning, 79, 151\u2013175.","journal-title":"Machine Learning"},{"key":"6432_CR3","doi-asserted-by":"crossref","unstructured":"Chen, Q., Du, Y., Tan, Z., Zhang, Y., & Wang, C.-J. (2020). Unsupervised domain adaptation with joint domain-adversarial reconstruction networks. In ECML\/PKDD.","DOI":"10.1007\/978-3-030-67661-2_38"},{"key":"6432_CR4","doi-asserted-by":"crossref","unstructured":"Chen, C., Fu, Z., Chen, Z., Jin, S., Cheng, Z., Jin, X., & Hua, X. (2020). Homm: Higher-order moment matching for unsupervised domain adaptation. In AAAI.","DOI":"10.1609\/aaai.v34i04.5745"},{"key":"6432_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), pp. 1416\u20131425.","DOI":"10.1109\/ICCV.2019.00150"},{"key":"6432_CR6","doi-asserted-by":"crossref","unstructured":"Cui, S., Wang, S., et al. (2020). Towards discriminability and diversity: Batch nuclear-norm maximization under label insufficient situations. In CVPR.","DOI":"10.1109\/CVPR42600.2020.00400"},{"key":"6432_CR7","doi-asserted-by":"crossref","unstructured":"Dai, S., Cheng, Y., et al. (2020). Contrastively smoothed class alignment for unsupervised domain adaptation. In ACCV.","DOI":"10.1007\/978-3-030-69538-5_17"},{"key":"6432_CR8","doi-asserted-by":"crossref","unstructured":"Deng, Z., Luo, Y., & Zhu, J. (2019). Cluster alignment with a teacher for unsupervised domain adaptation. In 2019 IEEE\/CVF international conference on computer vision (ICCV), pp. 9943\u20139952.","DOI":"10.1109\/ICCV.2019.01004"},{"key":"6432_CR9","unstructured":"Eastwood, C., Mason, I., Williams, C. K. I., & Sch\u00f6lkopf, B. (2021). Source-free adaptation to measurement shift via bottom-up feature restoration. In ICLR."},{"key":"6432_CR10","unstructured":"Feng, H., You, Z., Chen, M., Zhang, T.-Y., Zhu, M., Wu, F., Wu, C., & Chen, W. (2021). Kd3a: Unsupervised multi-source decentralized domain adaptation via knowledge distillation. In ICML."},{"key":"6432_CR11","unstructured":"Ganin, Y., & Lempitsky, V. (2015). Unsupervised domain adaptation by backpropagation. In ICML."},{"key":"6432_CR12","first-page":"59","volume":"17","author":"Y Ganin","year":"2016","unstructured":"Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., & Lempitsky, V. S. (2016). Domain-adversarial training of neural networks. The Journal of Machine Learning Research, 17, 59\u201315935.","journal-title":"The Journal of Machine Learning Research"},{"key":"6432_CR13","unstructured":"Gomes, R., Krause, A., & Perona, P. (2010). Discriminative clustering by regularized information maximization. In NIPS."},{"key":"6432_CR14","unstructured":"Goodfellow, I. J., Pouget-Abadie, J., et al. (2014). Generative adversarial networks. In NeruIPS."},{"key":"6432_CR15","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., et al. (2016). Deep residual learning for image recognition. In CVPR","DOI":"10.1109\/CVPR.2016.90"},{"key":"6432_CR16","unstructured":"Hoffman, J., Tzeng, E., et al. (2018). Cycada: Cycle-consistent adversarial domain adaptation. In ICML."},{"key":"6432_CR17","unstructured":"Hu, D., Liang, J., et al. (2020). Panda: Prototypical unsupervised domain adaptation. arXiv."},{"key":"6432_CR18","doi-asserted-by":"crossref","unstructured":"Jin, Y., Wang, X., et al. (2020). Minimum class confusion for versatile domain adaptation. In ECCV.","DOI":"10.1007\/978-3-030-58589-1_28"},{"key":"6432_CR19","doi-asserted-by":"crossref","unstructured":"Kang, G., Jiang, L., et al. (2019). Contrastive adaptation network for unsupervised domain adaptation. In CVPR.","DOI":"10.1109\/CVPR.2019.00503"},{"key":"6432_CR20","doi-asserted-by":"crossref","unstructured":"Kim, Y., Cho, D., Han, K., Panda, P., & Hong, S. (2021). Domain adaptation without source data. IEEE Transactions on Artificial Intelligence.","DOI":"10.1109\/TAI.2021.3110179"},{"key":"6432_CR21","unstructured":"Kumar, A., Sattigeri, P., Wadhawan, K., Karlinsky, L., Feris, R., Freeman, W., & Wornell, G. (2018). Co-regularized alignment for unsupervised domain adaptation. In NeurIPS."},{"key":"6432_CR22","doi-asserted-by":"crossref","unstructured":"Kundu, J.N., Kulkarni, A.R., Singh, A., Jampani, V., & Babu, R.V. (2021). Generalize then adapt: Source-free domain adaptive semantic segmentation. In ICCV.","DOI":"10.1109\/ICCV48922.2021.00696"},{"key":"6432_CR23","doi-asserted-by":"crossref","unstructured":"Kundu, J. N., Venkat, N., RahulM., V., & Babu, R. V. (2020). Universal source-free domain adaptation. In 2020 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp. 4543\u20134552.","DOI":"10.1109\/CVPR42600.2020.00460"},{"key":"6432_CR24","doi-asserted-by":"crossref","unstructured":"Kurmi, V., Subramanian, V. K., & Namboodiri, V. P. (2021). Domain impression: A source data free domain adaptation method. In 2021 IEEE Winter conference on applications of computer vision (WACV), pp. 615\u2013625.","DOI":"10.1109\/WACV48630.2021.00066"},{"key":"6432_CR25","doi-asserted-by":"crossref","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition.","DOI":"10.1109\/5.726791"},{"key":"6432_CR26","doi-asserted-by":"crossref","unstructured":"Lee, C.-Y., Batra, T., Baig, M.H., & Ulbricht, D. (2019) Sliced wasserstein discrepancy for unsupervised domain adaptation. In 2019 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp. 10277\u201310287.","DOI":"10.1109\/CVPR.2019.01053"},{"key":"6432_CR27","doi-asserted-by":"crossref","unstructured":"Lee, C.-Y., Batra, T., Baig, M. H., & Ulbricht, D. (2019). Sliced wasserstein discrepancy for unsupervised domain adaptation. In CVPR.","DOI":"10.1109\/CVPR.2019.01053"},{"key":"6432_CR28","doi-asserted-by":"crossref","unstructured":"Li, J., Chen, E., Ding, Z., Zhu, L., Lu, K., & Shen, H. T. (2020). Maximum density divergence for domain adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence.","DOI":"10.1109\/TPAMI.2020.2991050"},{"key":"6432_CR29","doi-asserted-by":"crossref","unstructured":"Li, X., Chen, W., Xie, D., Yang, S., Yuan, P., Pu, S., & Zhuang, Y. (2021). A free lunch for unsupervised domain adaptive object detection without source data. In AAAI.","DOI":"10.1609\/aaai.v35i10.17029"},{"key":"6432_CR30","doi-asserted-by":"crossref","unstructured":"Li, R., Jiao, Q., et al. (2020). Model adaptation: Unsupervised domain adaptation without source data. In CVPR.","DOI":"10.1109\/CVPR42600.2020.00966"},{"key":"6432_CR31","doi-asserted-by":"crossref","unstructured":"Li, X., Li, J., Zhu, L., Wang, G., & Huang, Z. (2021). Imbalanced source-free domain adaptation. In Proceedings of the 29th ACM international conference on multimedia.","DOI":"10.1145\/3474085.3475487"},{"key":"6432_CR32","doi-asserted-by":"publisher","first-page":"2935","DOI":"10.1109\/TPAMI.2017.2773081","volume":"40","author":"Z Li","year":"2018","unstructured":"Li, Z., & Hoiem, D. (2018). Learning without forgetting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 2935\u20132947.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"6432_CR33","doi-asserted-by":"crossref","unstructured":"Liang, J., et al. (2021). Source data-absent unsupervised domain adaptation through hypothesis transfer and labeling transfer. T-PAMI.","DOI":"10.1109\/TPAMI.2021.3103390"},{"key":"6432_CR34","unstructured":"Liang, J., Hu, D., et al. (2020). Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation. In ICML."},{"key":"6432_CR35","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhang, W., & Wang, J. (2021). Source-free domain adaptation for semantic segmentation. In CVPR.","DOI":"10.1109\/CVPR46437.2021.00127"},{"key":"6432_CR36","unstructured":"Long, M., Cao, Z., et al. (2018). Conditional adversarial domain adaptation. In NeruIPS."},{"key":"6432_CR37","unstructured":"Long, M., Cao, Y., Wang, J., & Jordan, M. I. (2015). Learning transferable features with deep adaptation networks. In ICML."},{"key":"6432_CR38","unstructured":"Long, M., Zhu, H., Wang, J., & Jordan, M. I. (2017). Deep transfer learning with joint adaptation networks. In ICML."},{"key":"6432_CR39","unstructured":"M\u00fcller, R., Kornblith, S., et al. (2019). When does label smoothing help? In NeurIPS."},{"key":"6432_CR40","doi-asserted-by":"crossref","unstructured":"Pan, F., et al. (2020). Unsupervised intra-domain adaptation for semantic segmentation through self-supervision. In CVPR.","DOI":"10.1109\/CVPR42600.2020.00382"},{"key":"6432_CR41","doi-asserted-by":"crossref","unstructured":"Pan, Y., Yao, T., et al. (2019). Transferrable prototypical networks for unsupervised domain adaptation. In CVPR.","DOI":"10.1109\/CVPR.2019.00234"},{"key":"6432_CR42","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22, 1345\u20131359.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"6432_CR43","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.neunet.2019.01.012","volume":"113","author":"GI Parisi","year":"2019","unstructured":"Parisi, G. I., Kemker, R., Part, J. L., Kanan, C., & Wermter, S. (2019). Continual lifelong learning with neural networks: A review. Neural networks, 113, 54\u201371.","journal-title":"Neural networks"},{"key":"6432_CR44","doi-asserted-by":"crossref","unstructured":"Peng, X., Usman, B., Kaushik, N., Wang, D., Hoffman, J., & Saenko, K. (2018). Visda: A synthetic-to-real benchmark for visual domain adaptation. In 2018 IEEE\/CVF conference on computer vision and pattern recognition workshops (CVPRW), pp. 2102\u201321025","DOI":"10.1109\/CVPRW.2018.00271"},{"key":"6432_CR45","unstructured":"Saito, K., Ushiku, Y., & Harada, T. (2017). Asymmetric tri-training for unsupervised domain adaptation. In ICML."},{"key":"6432_CR46","unstructured":"Saito, K., Ushiku, Y., Harada, T., & Saenko, K. (2018). Adversarial dropout regularization. In ICLR."},{"key":"6432_CR47","doi-asserted-by":"crossref","unstructured":"Saito, K., Watanabe, K., et al. (2018). Maximum classifier discrepancy for unsupervised domain adaptation. In CVPR.","DOI":"10.1109\/CVPR.2018.00392"},{"key":"6432_CR48","doi-asserted-by":"crossref","unstructured":"Shen, J., Qu, Y., Zhang, W., & Yu, Y. (2018). Wasserstein distance guided representation learning for domain adaptation. In AAAI.","DOI":"10.1609\/aaai.v32i1.11784"},{"key":"6432_CR49","doi-asserted-by":"crossref","unstructured":"Sun, B., Feng, J., & Saenko, K. (2016). Return of frustratingly easy domain adaptation. In AAAI.","DOI":"10.1609\/aaai.v30i1.10306"},{"key":"6432_CR50","doi-asserted-by":"crossref","unstructured":"Tang, H., Chen, K., & Jia, K. (2020). Unsupervised domain adaptation via structurally regularized deep clustering. In CVPR.","DOI":"10.1109\/CVPR42600.2020.00875"},{"key":"6432_CR51","doi-asserted-by":"crossref","unstructured":"Tzeng, E., Hoffman, J., Saenko, K., & Darrell, T. (2017). Adversarial discriminative domain adaptation. In CVPR.","DOI":"10.1109\/CVPR.2017.316"},{"key":"6432_CR52","doi-asserted-by":"crossref","unstructured":"Venkateswara, H., Eusebio, J., et al. (2017). Deep hashing network for unsupervised domain adaptation. In CVPR.","DOI":"10.1109\/CVPR.2017.572"},{"key":"6432_CR53","doi-asserted-by":"crossref","unstructured":"Xu, R., Li, G., et al. (2019). Larger norm more transferable: An adaptive feature norm approach for unsupervised domain adaptation. In ICCV.","DOI":"10.1109\/ICCV.2019.00151"},{"key":"6432_CR54","unstructured":"Yang, S., Wang, Y., et al. (2020). Unsupervised domain adaptation without source data by casting a bait. arXiv."},{"key":"6432_CR55","doi-asserted-by":"crossref","unstructured":"Yang, S., Wang, Y., Weijer, J., Herranz, L., & Jui, S. (2021). Generalized source-free domain adaptation. In ICCV.","DOI":"10.1109\/ICCV48922.2021.00885"},{"key":"6432_CR56","doi-asserted-by":"crossref","unstructured":"Yang, G., Xia, H., et al. (2020). Bi-directional generation for unsupervised domain adaptation. In AAAI.","DOI":"10.1609\/aaai.v34i04.6137"},{"key":"6432_CR57","doi-asserted-by":"crossref","unstructured":"Ye, M., Zhang, J., Ouyang, J., & Yuan, D. (2021). Source data-free unsupervised domain adaptation for semantic segmentation. In Proceedings of the 29th ACM international conference on multimedia.","DOI":"10.1145\/3474085.3475384"},{"key":"6432_CR58","doi-asserted-by":"crossref","unstructured":"You, F., Li, J., Zhu, L., Chen, Z., & Huang, Z. (2021). Domain adaptive semantic segmentation without source data. In Proceedings of the 29th ACM international conference on multimedia.","DOI":"10.1145\/3474085.3475482"},{"key":"6432_CR59","unstructured":"Zellinger, W., Grubinger, T., Lughofer, E., Natschl\u00e4ger, T., & Saminger-Platz, S. (2017). Central moment discrepancy (cmd) for domain-invariant representation learning. In ICLR."},{"key":"6432_CR60","unstructured":"Zhang, H., Ciss\u00e9, M., Dauphin, Y., & Lopez-Paz, D. (2018). mixup: Beyond empirical risk minimization. In ICLR."},{"key":"6432_CR61","unstructured":"Zhang, Y., Deng, B., Tang, H., Zhang, L., & Jia, K. (2020). Unsupervised multi-class domain adaptation: Theory, algorithms, and practice. IEEE Transactions on Pattern Analysis and Machine Intelligence."},{"key":"6432_CR62","unstructured":"Zhang, Y., Liu, T., et al. (2019). Bridging theory and algorithm for domain adaptation. In ICML."}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-023-06432-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10994-023-06432-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-023-06432-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T18:04:11Z","timestamp":1764266651000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10994-023-06432-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,20]]},"references-count":62,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["6432"],"URL":"https:\/\/doi.org\/10.1007\/s10994-023-06432-8","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"value":"0885-6125","type":"print"},{"value":"1573-0565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,20]]},"assertion":[{"value":"12 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 August 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 October 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 December 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"All authors agreed with the content and that all gave explicit consent to submit and for publication.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate and for publication:"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}