Journal of Shandong University(Engineering Science) ›› 2023, Vol. 53 ›› Issue (2): 102-108.doi: 10.6040/j.issn.1672-3961.0.2022.321
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LIU Ziyi, CUI Chaoran*, MENG Fan'an, LIN Peiguang
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[1] | LUO Y, ZHENG L, GUAN T, et al. Taking a closer look at domain shift: category-level adversaries for semantics consistent domain adaptation[C] //Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA: IEEE, 2019: 2507-2516. |
[2] | LIANG J, HU D P, FENG J S. Do we really need to access the source data? Source hypothesis transfer for unsupervised domain adaptation[C] //Proceedings of International Conference on Machine Learning. Vienna, Austria: PMLR, 2020: 6028-6039. |
[3] | HUANG J X, GUAN D Y, XIAO A R, et al. Model adaptation:historical contrastive learning for unsupervised domain adaptation without source data[J]. Advances in Neural Information Processing Systems, 2021, 34: 3635-3649. |
[4] | ISHII M, SUGIYAMA M. Source-free domain adaptation via distributional alignment by matching batch normalization statistics[EB/OL].(2022-04-24)[2022-09-25]. https://arxiv.org/abs/2101.10842. |
[5] | LIANG J, HU D P, FENG J S. Domain adaptation with auxiliary target domain-oriented classifier[C] //Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2021: 16632-16642. |
[6] | GOODFELLOW I, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial networks[J]. Communi-cations of the ACM, 2020, 63(11): 139-144. |
[7] | LI J J, CHEN E P, DING Z M, et al. Cycle-consistent conditional adversarial transfer networks[C] //Proceedings of the 27th ACM International Conference on Multimedia. Nice, France: ACMM, 2019: 747-755. |
[8] | HOFFMAN J, TZENG E, PARK T, et al. CyCADA:cycle-consistent adversarial domain adaptation[C] // International Conference on Machine Learning. Stockholm, Sweden: PMLR, 2018: 1989-1998. |
[9] | KURMI V K, SUBRAMANIAN V K, NAMBOODIRI V P. Domain impression:a source data free domain adaptation method[C] //Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. Waikoloa, USA: IEEE, 2021: 615-625. |
[10] | LI R, JIAO Q F, CAO W M, et al. Model adaptation:unsupervised domain adaptation without source data[C] //Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, USA: IEEE, 2020: 9641-9650. |
[11] | AHMED S M, RAYCHAUDHURI D S, PAUL S, et al. Unsupervised multi-source domain adaptation without access to source data[C] //Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2021: 10103-10112. |
[12] | DONG J H, FANG Z, LIU A J, et al. Confident anchor-induced multi-source free domain adaptation[J]. Advances in Neural Information Processing Systems, 2021, 34: 2848-2860. |
[13] | HOFFMAN J, MOHRI M, ZHANG N. Algorithms and theory for multiple-source adaptation[J]. Advances in Neural Information Processing Systems, 2018, 31: 8246-8256. |
[14] | ZHONG Z, ZHENG L, LUO Z M, et al. Invariance matters: exemplar memory for domain adaptive person re-identification[C] //Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA: IEEE, 2019: 598-607. |
[15] | GUO C, PLEISS G, SUN Y, et al. On calibration of modern neural networks[C] //Proceedings of International Conference on Machine Learning. Sydney, Australia: PMLR, 2017: 1321-1330. |
[16] | LEE D H. Pseudo-label:the simple and efficient semi-supervised learning method for deep neural networks[C] //Proceedings of Workshop on Challenges in Representation Learning, ICML. Atlanta, USA: PMLR, 2013, 3(2): 896. |
[17] | GONG B Q, SHI Y, SHA F, et al. Geodesic flow kernel for unsupervised domain adaptation[C] //2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence, USA: IEEE, 2012: 2066-2073. |
[18] | VENKATESWARA H, EUSEBIO J, CHAKRABORTY S, et al. Deep hashing network for unsupervised domain adaptation[C] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE, 2017: 5018-5027. |
[19] | YANG S, WANG Y, VAN DE WEIJER J, et al. Unsupervised domain adaptation without source data by casting a bait[EB/OL].(2021-10-29)[2022-09-25]. https://arxiv.org/abs/2010.12427. |
[20] | KIM Y, CHO D, HAN K, et al. Domain adaptation without source data[J]. IEEE Transactions on Artificial Intelligence, 2021, 2(6): 508-518. |
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