14.1.8.3 Semi-Supervised Domain Adaptation

Xiao, M., Guo, Y.,
Feature Space Independent Semi-Supervised Domain Adaptation via Kernel Matching,
PAMI(37), No. 1, January 2015, pp. 54-66.
IEEE DOI 1412
Adaptation models BibRef

Samat, A.[Alim], Persello, C.[Claudio], Gamba, P.[Paolo], Liu, S.C.[Si-Cong], Abuduwaili, J.[Jilili], Li, E.[Erzhu],
Supervised and Semi-Supervised Multi-View Canonical Correlation Analysis Ensemble for Heterogeneous Domain Adaptation in Remote Sensing Image Classification,
RS(9), No. 4, 2017, pp. xx-yy.
DOI Link 1705
BibRef

Pereira, L.A.M.[Luís A.M.], da Silva Torres, R.[Ricardo],
Semi-supervised transfer subspace for domain adaptation,
PR(75), No. 1, 2018, pp. 235-249.
Elsevier DOI 1712
Cross-domain knowledge transfer BibRef

Ding, Z., Nasrabadi, N.M., Fu, Y.,
Semi-supervised Deep Domain Adaptation via Coupled Neural Networks,
IP(27), No. 11, November 2018, pp. 5214-5224.
IEEE DOI 1809
feature extraction, learning (artificial intelligence), neural nets, pattern classification, probability, deep neural networks BibRef

Wang, W.[Wei], Wang, H.[Hao], Zhang, Z.X.[Zhao-Xiang], Zhang, C.[Chen], Gao, Y.[Yang],
Semi-Supervised Domain Adaptation Via Fredholm Integral Based Kernel Methods,
PR(85), 2019, pp. 185-197.
Elsevier DOI 1810
Domain adaptation, Semi-supervised learning, Multiple kernel learning, Hilbert space embedding of distributions BibRef

Li, L.M.[Li-Min], Zhang, Z.Y.[Zhen-Yue],
Semi-Supervised Domain Adaptation by Covariance Matching,
PAMI(41), No. 11, November 2019, pp. 2724-2739.
IEEE DOI 1910
Kernel, Convergence, Adaptation models, Mathematical model, Eigenvalues and eigenfunctions, Manifolds, domain adaptation BibRef

Li, W.[Wei], Wang, M.[Meng], Wang, H.B.[Hong-Bin], Zhang, Y.[Yafei],
Object detection based on semi-supervised domain adaptation for imbalanced domain resources,
MVA(31), No. 3, March 2020, pp. Article18.
WWW Link. 2004
BibRef

Wang, W.[Wei], Chen, S.L.[Sheng-Lun], Xiang, Y.K.[Yuan-Kai], Sun, J.[Jing], Li, H.J.[Hao-Jie], Wang, Z.H.[Zhi-Hui], Sun, F.M.[Fu-Ming], Ding, Z.M.[Zheng-Ming], Li, B.[Baopu],
Sparsely-labeled source assisted domain adaptation,
PR(112), 2021, pp. 107803.
Elsevier DOI 2102
Domain adaptation, Sparsely-labeled source, Semi-supervised clustering, Label propagation BibRef

Fang, Z.[Zhen], Lu, J.[Jie], Liu, F.[Feng], Zhang, G.Q.[Guang-Quan],
Semi-Supervised Heterogeneous Domain Adaptation: Theory and Algorithms,
PAMI(45), No. 1, January 2023, pp. 1087-1105.
IEEE DOI 2212
Classification algorithms, Task analysis, Kernel, Training data, Training, Picture archiving and communication systems, Manifolds, classification BibRef

Yang, S.Q.[Shi-Qi], Wang, Y.X.[Ya-Xing], Herranz, L.[Luis], Jui, S.L.[Shang-Ling], van de Weijer, J.[Joost],
Casting a BAIT for offline and online source-free domain adaptation,
CVIU(234), 2023, pp. 103747.
Elsevier DOI 2307
BibRef
Earlier: A1, A2, A5, A3, A4:
Generalized Source-free Domain Adaptation,
ICCV21(8958-8967)
IEEE DOI 2203
Source-free domain adaptation, Online domain adaptation. Training, Adaptation models, Codes, Data models, Transfer/Low-shot/Semi/Unsupervised Learning, Recognition and classification BibRef

Li, J.C.[Ji-Chang], Li, G.B.[Guan-Bin], Yu, Y.Z.[Yi-Zhou],
Adaptive Betweenness Clustering for Semi-Supervised Domain Adaptation,
IP(32), 2023, pp. 5580-5594.
IEEE DOI 2310
BibRef

Li, J.C.[Ji-Chang], Li, G.B.[Guan-Bin], Yu, Y.Z.[Yi-Zhou],
Inter-domain mixup for semi-supervised domain adaptation,
PR(146), 2024, pp. 110023.
Elsevier DOI 2311
Semi-supervised domain adaptation, Inter-domain mixup, Neighborhood expansion BibRef

Li, J.C.[Ji-Chang], Li, G.B.[Guan-Bin], Shi, Y.[Yemin], Yu, Y.Z.[Yi-Zhou],
Cross-Domain Adaptive Clustering for Semi-Supervised Domain Adaptation,
CVPR21(2505-2514)
IEEE DOI 2111
Training, Adaptation models, Training data, Benchmark testing, Data models, Adversarial machine learning BibRef

Park, S.[Sunghong], Kim, M.J.[Myung Jun], Park, K.[Kanghee], Shin, H.J.[Hyun-Jung],
Mutual Domain Adaptation,
PR(145), 2024, pp. 109919.
Elsevier DOI 2311
Domain adaptation, Semi-supervised learning, Label propagation, Pseudo-labeling BibRef

Gu, X.[Xiang], Sun, J.[Jian], Xu, Z.B.[Zong-Ben],
Unsupervised and Semi-Supervised Robust Spherical Space Domain Adaptation,
PAMI(46), No. 3, March 2024, pp. 1757-1774.
IEEE DOI 2402
BibRef
Earlier:
Spherical Space Domain Adaptation With Robust Pseudo-Label Loss,
CVPR20(9098-9107)
IEEE DOI 2008
Training, Face recognition, Labeling, Feature extraction, Task analysis, Target recognition, Sun, Domain adaptation, reweighted adversarial training. Robustness, Mixture models, Entropy, Data models, Labeling BibRef

Liu, X.[Xuan], Huang, Y.[Ying], Wang, H.[Hao], Xiao, Z.[Zheng], Zhang, S.[Shigeng],
Universal and Scalable Weakly-Supervised Domain Adaptation,
IP(33), 2024, pp. 1313-1325.
IEEE DOI 2402
Noise measurement, Feature extraction, Adaptation models, Training, Scalability, Generators, Data models, pseudo-labels BibRef

Han, Z.[Zheng], Zhu, X.B.[Xia-Bin], Yang, C.[Chun], Fang, Z.[Zhiyu], Qin, J.Y.[Jing-Yan], Yin, X.[Xucheng],
Semi-supervised domain adaptation via subspace exploration,
IET-CV(18), No. 3, 2024, pp. 370-380.
DOI Link 2404
image classification, image representation BibRef

Rahman, M.M.[Md Mahmudur], Panda, R.[Rameswar], Ul Alam, M.A.[Mohammad Arif],
Semi-Supervised Domain Adaptation with Auto-Encoder via Simultaneous Learning,
WACV23(402-411)
IEEE DOI 2302
Training, Adaptation models, Computational modeling, Linear programming, Convergence, Vision + language and/or other modalities BibRef

Pérez, G.[Gustavo], Maji, S.[Subhransu],
Domain Adaptors for Hyperspectral Images,
ICPR22(3048-3055)
IEEE DOI 2212
Training, Adaptation models, Color, Benchmark testing, Semisupervised learning, Transformers BibRef

Kuchibhotla, H.C.[Hari Chandana], Malagi, S.S.[Sumitra S.], Chandhok, S.[Shivam], Balasubramanian, V.N.[Vineeth N.],
Unseen Classes at a Later Time? No Problem,
CVPR22(9235-9244)
IEEE DOI 2210
Adaptation models, Protocols, Bidirectional control, Benchmark testing, Pattern recognition, Unsupervised learning, Self- semi- meta- unsupervised learning BibRef

Li, S.T.[Shuang-Tong], Zhou, T.Y.[Tian-Yi], Tian, X.[Xinmei], Tao, D.C.[Da-Cheng],
Learning to Collaborate in Decentralized Learning of Personalized Models,
CVPR22(9756-9765)
IEEE DOI 2210
Training, Adaptation models, Costs, Network topology, Computational modeling, Aggregates, Image edge detection, Self- semi- meta- Machine learning BibRef

Chen, L.[Liang], Lou, Y.H.[Yi-Hang], He, J.Z.[Jian-Zhong], Bai, T.[Tao], Deng, M.H.[Ming-Hua],
Geometric Anchor Correspondence Mining with Uncertainty Modeling for Universal Domain Adaptation,
CVPR22(16113-16122)
IEEE DOI 2210
Representation learning, Manifolds, Adaptation models, Uncertainty, Computational modeling, Logic gates, Representation learning, Self- semi- meta- Transfer/low-shot/long-tail learning BibRef

Xie, M.[Ming], Li, Y.X.[Yu-Xi], Wang, Y.[Yabiao], Luo, Z.K.[Ze-Kun], Gan, Z.[Zhenye], Sun, Z.Y.[Zhong-Yi], Chi, M.[Mingmin], Wang, C.J.[Cheng-Jie], Wang, P.[Pei],
Learning Distinctive Margin toward Active Domain Adaptation,
CVPR22(7983-7992)
IEEE DOI 2210
Training, Support vector machines, Adaptation models, Analytical models, Computational modeling, Scalability, Self- semi- meta- unsupervised learning BibRef

Sun, T.[Tao], Lu, C.[Cheng], Zhang, T.S.[Tian-Shuo], Ling, H.B.[Hai-Bin],
Safe Self-Refinement for Transformer-based Domain Adaptation,
CVPR22(7181-7190)
IEEE DOI 2210
Training, Adaptation models, Computational modeling, Benchmark testing, Predictive models, Transformers, Data models, Self- semi- meta- unsupervised learning BibRef

Wang, Q.[Qin], Fink, O.[Olga], Van Gool, L.J.[Luc J.], Dai, D.X.[Deng-Xin],
Continual Test-Time Domain Adaptation,
CVPR22(7191-7201)
IEEE DOI 2210
Adaptation models, Codes, Computational modeling, Neurons, Data models, Entropy, Transfer/low-shot/long-tail learning, Self- semi- meta- unsupervised learning BibRef

Ding, N.[Ning], Xu, Y.X.[Yi-Xing], Tang, Y.[Yehui], Xu, C.[Chao], Wang, Y.H.[Yun-He], Tao, D.C.[Da-Cheng],
Source-Free Domain Adaptation via Distribution Estimation,
CVPR22(7202-7212)
IEEE DOI 2210
Representation learning, Data privacy, Estimation, Training data, Benchmark testing, Data models, Self- semi- meta- unsupervised learning BibRef

Shen, Y.F.[Yue-Fan], Yang, Y.C.[Yan-Chao], Yan, M.[Mi], Wang, H.[He], Zheng, Y.[Youyi], Guibas, L.J.[Leonidas J.],
Domain Adaptation on Point Clouds via Geometry-Aware Implicits,
CVPR22(7213-7222)
IEEE DOI 2210
Point cloud compression, Training, Shape, Neural networks, Robot sensing systems, Transfer/low-shot/long-tail learning, Self- semi- meta- unsupervised learning BibRef

Saito, K.[Kuniaki], Saenko, K.[Kate],
OVANet: One-vs-All Network for Universal Domain Adaptation,
ICCV21(8980-8989)
IEEE DOI 2203
Entropy, Transfer/Low-shot/Semi/Unsupervised Learning, Recognition and classification BibRef

Li, K.[Kai], Liu, C.[Chang], Zhao, H.[Handong], Zhang, Y.[Yulun], Fu, Y.[Yun],
ECACL: A Holistic Framework for Semi-Supervised Domain Adaptation,
ICCV21(8558-8567)
IEEE DOI 2203
Adaptation models, Codes, Perturbation methods, Computational modeling, Data models, BibRef

Liang, J.[Jian], Hu, D.P.[Da-Peng], Feng, J.S.[Jia-Shi],
Domain Adaptation with Auxiliary Target Domain-Oriented Classifier,
CVPR21(16627-16637)
IEEE DOI 2111
Handheld computers, Semantics, Focusing, Semisupervised learning, Benchmark testing, Pattern recognition BibRef

Li, B.[Bo], Wang, Y.Z.[Ye-Zhen], Zhang, S.H.[Shang-Hang], Li, D.S.[Dong-Sheng], Keutzer, K.[Kurt], Darrell, T.J.[Trevor J.], Zhao, H.[Han],
Learning Invariant Representations and Risks for Semi-supervised Domain Adaptation,
CVPR21(1104-1113)
IEEE DOI 2111
Training, Shape, Snow, Supervised learning, Fasteners, Minimization, Classification algorithms BibRef

Kim, Y.[Yoonhyung], Kim, C.[Changick],
Semi-Supervised Domain Adaptation via Selective Pseudo Labeling and Progressive Self-Training,
ICPR21(1059-1066)
IEEE DOI 2105
Training, Semantics, Object detection, Pattern recognition, Noise robustness, Noise measurement, Reliability BibRef

Yang, L.[Luyu], Wang, Y.[Yan], Gao, M.F.[Ming-Fei], Shrivastava, A.[Abhinav], Weinberger, K.Q.[Kilian Q.], Chao, W.L.[Wei-Lun], Lim, S.N.[Ser-Nam],
Deep Co-Training with Task Decomposition for Semi-Supervised Domain Adaptation,
ICCV21(8886-8896)
IEEE DOI 2203
Training, Adaptation models, Codes, Art, Semisupervised learning, Data models, Transfer/Low-shot/Semi/Unsupervised Learning, Recognition and classification BibRef

Kim, T.[Taekyung], Kim, C.[Changick],
Attract, Perturb, and Explore: Learning a Feature Alignment Network for Semi-supervised Domain Adaptation,
ECCV20(XIV:591-607).
Springer DOI 2011
BibRef

He, G., Liu, X., Fan, F., You, J.,
Classification-aware Semi-supervised Domain Adaptation,
MULWS20(4147-4156)
IEEE DOI 2008
Training, Emotion recognition, Visualization, Task analysis, Reliability, Training data BibRef

Saito, K., Kim, D., Sclaroff, S., Darrell, T.J., Saenko, K.,
Semi-Supervised Domain Adaptation via Minimax Entropy,
ICCV19(8049-8057)
IEEE DOI 2004
Code, Domain Adaption.
HTML Version. convolutional neural nets, entropy, feature extraction, minimax techniques, pattern classification, supervised learning, Computational modeling BibRef

Liu, P., Cheng, C., Feng, Y., Shao, X., Zhou, X.,
Semi-supervised domain adaptation via convolutional neural network,
ICIP17(2841-2845)
IEEE DOI 1803
Adaptation models, Benchmark testing, Feature extraction, Image recognition, Mathematical model, Standards, Training BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Unsupervised Domain Adaptation .