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
Han, W.[Wei],
Feng, R.[Ruyi],
Wang, L.Z.[Li-Zhe],
Cheng, Y.F.[Ya-Fan],
A semi-supervised generative framework with deep learning features
for high-resolution remote sensing image scene classification,
PandRS(145), 2018, pp. 23-43.
Elsevier DOI
1810
Scene classification, Deep learning, Self-label,
High resolution remote sensing images
BibRef
Chen, W.,
Hsu, T.H.,
Tsai, Y.H.,
Chen, M.,
Wang, Y.F.,
Transfer Neural Trees: Semi-Supervised Heterogeneous Domain
Adaptation and Beyond,
IP(28), No. 9, Sep. 2019, pp. 4620-4633.
IEEE DOI
1908
image classification, learning (artificial intelligence),
pattern classification, trees (mathematics), zero-shot learning
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.F.[Ya-Fei],
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
Huang, W.[Wei],
Shi, Y.L.[Yi-Lei],
Xiong, Z.T.[Zhi-Tong],
Wang, Q.[Qi],
Zhu, X.X.[Xiao Xiang],
Semi-supervised bidirectional alignment for Remote Sensing
cross-domain scene classification,
PandRS(195), 2023, pp. 192-203.
Elsevier DOI
2301
Remote sensing, Semi-supervised domain adaptation,
Cross-domain classification, Bidirectional sample-class alignment
BibRef
Li, X.J.[Xing-Jian],
Abuduweili, A.[Abulikemu],
Shi, H.[Humphrey],
Yang, P.K.[Peng-Kun],
Dou, D.J.[De-Jing],
Xiong, H.Y.[Hao-Yi],
Xu, C.Z.[Cheng-Zhong],
Semi-supervised transfer learning with hierarchical
self-regularization,
PR(144), 2023, pp. 109831.
Elsevier DOI
2310
BibRef
Earlier: A2, A1, A3, A7, A5, Only:
Adaptive Consistency Regularization for Semi-Supervised Transfer
Learning,
CVPR21(6919-6928)
IEEE DOI
2111
Semi-supervised learning, Transfer learning, Fine-tuning,
Deep learning, Hierarchical consistency, Adaptive sample selection.
Adaptation models, Codes, Supervised learning, Benchmark testing
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.Y.[Zhi-Yu],
Qin, J.Y.[Jing-Yan],
Yin, X.C.[Xu-Cheng],
Semi-supervised domain adaptation via subspace exploration,
IET-CV(18), No. 3, 2024, pp. 370-380.
DOI Link
2404
image classification, image representation
BibRef
Qiao, Z.Y.[Zi-Yue],
Xiao, M.[Meng],
Guo, W.Y.[Wei-Yu],
Luo, X.[Xiao],
Xiong, H.[Hui],
Information filtering and interpolating for semi-supervised graph
domain adaptation,
PR(153), 2024, pp. 110498.
Elsevier DOI Code:
WWW Link.
2405
Transfer learning, Semi-supervised learning, Domain adaptation
BibRef
Lan, M.C.[Meng-Cheng],
Meng, M.[Min],
Yu, J.[Jun],
Wu, J.G.[Ji-Gang],
Learning to Discover Knowledge: A Weakly-Supervised Partial Domain
Adaptation Approach,
IP(33), 2024, pp. 4090-4103.
IEEE DOI Code:
WWW Link.
2407
Noise measurement, Task analysis, Training, Adaptation models, Noise,
Annotations, Standards, Domain adaptation,
distribution adaptation
BibRef
Luo, H.[Hao],
Tian, Z.Q.[Zhi-Qiang],
Zhang, K.B.[Kai-Bing],
Wang, G.[Guofa],
Du, S.[Shaoyi],
Semi-Supervised Domain Adaptation via Joint Transductive and
Inductive Subspace Learning,
MultMed(26), 2024, pp. 10431-10445.
IEEE DOI
2411
Training, Task analysis, Tensors, Learning systems,
Transfer learning, Minimization methods, Feature extraction,
semi-supervised domain adaptation
BibRef
Kechaou, M.[Marwa],
Alaya, M.Z.[Mokhtar Z.],
Hérault, R.[Romain],
Gasso, G.[Gilles],
Adversarial Semi-supervised domain adaptation for semantic
segmentation: A new role for labeled target samples,
CVIU(253), 2025, pp. 104305.
Elsevier DOI
2503
Domain Adaptation, Semantic Segmentation,
Semi-supervised learning, Adversarial learning
BibRef
Zhou, H.[Heng],
Zhong, P.[Ping],
Wave-based cross-phase representation for weakly supervised
classification,
IVC(158), 2025, pp. 105527.
Elsevier DOI
2505
Weakly supervised learning, Wave representation,
Feature interaction, Image classification, Token mixing
BibRef
Wu, Z.Y.[Zhi-Yu],
Cui, J.S.[Jin-Shi],
Image-feature Weak-to-strong Consistency: An Enhanced Paradigm for
Semi-supervised Learning,
ECCV24(XV: 372-388).
Springer DOI
2412
BibRef
Song, J.[Junha],
Kim, T.S.[Tae Soo],
Kim, J.[Junha],
Nam, G.[Gunhee],
Kooi, T.[Thijs],
Choo, J.[Jaegul],
Is User Feedback Always Informative? Retrieval Latent Defending for
Semi-supervised Domain Adaptation Without Source Data,
ECCV24(XXV: 72-92).
Springer DOI
2412
BibRef
Zhang, W.Y.[Wen-Yu],
Liu, Q.[Qingmu],
Cong, F.O.W.[Felix Ong Wei],
Ragab, M.[Mohamed],
Foo, C.S.[Chuan-Sheng],
Universal Semi-Supervised Domain Adaptation by Mitigating
Common-Class Bias,
CVPR24(23912-23921)
IEEE DOI
2410
Adaptation models, Computational modeling, Machine learning,
Benchmark testing, Data models,
semi-supervised domain adaptation
BibRef
Yan, Z.Z.[Zi-Zheng],
Wu, Y.S.[Yu-Shuang],
Qin, Y.P.[Yi-Peng],
Han, X.G.[Xiao-Guang],
Cui, S.G.[Shu-Guang],
Li, G.B.[Guan-Bin],
Universal Semi-supervised Model Adaptation via Collaborative
Consistency Training,
WACV24(861-871)
IEEE DOI
2404
Training, Adaptation models, Computational modeling, Collaboration,
Predictive models, Semisupervised learning, Algorithms
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
Lee, H.J.[Hyun-Jae],
Lee, G.[Gihyeon],
Kim, J.[Junhwan],
Cho, S.J.[Sung-Jun],
Kim, D.[Dohyun],
Yoo, D.[Donggeun],
Improving Multi-fidelity Optimization with a Recurring Learning Rate
for Hyperparameter Tuning,
WACV23(2308-2317)
IEEE DOI
2302
Training, Schedules, Transfer learning, Optimization methods,
Semisupervised learning, Convolutional neural networks,
visual reasoning
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, 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.B.[Ya-Biao],
Luo, Z.K.[Ze-Kun],
Gan, Z.Y.[Zhen-Ye],
Sun, Z.Y.[Zhong-Yi],
Chi, M.M.[Ming-Min],
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
Ding, N.[Ning],
Xu, Y.X.[Yi-Xing],
Tang, Y.H.[Ye-Hui],
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.Y.[You-Yi],
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
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,
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
Marini, N.[Niccolò],
Otálora, S.[Sebastian],
Müller, H.[Henning],
Atzori, M.[Manfredo],
Semi-supervised Learning with a Teacher-Student Paradigm for
Histopathology Classification: A Resource to Face Data Heterogeneity
and Lack of Local Annotations,
AIDP20(105-119).
Springer DOI
2103
BibRef
Chen, L.C.[Liang-Chieh],
Lopes, R.G.[Raphael Gontijo],
Cheng, B.[Bowen],
Collins, M.D.[Maxwell D.],
Cubuk, E.D.[Ekin D.],
Zoph, B.[Barret],
Adam, H.[Hartwig],
Shlens, J.[Jonathon],
Naive-Student: Leveraging Semi-supervised Learning in Video Sequences
for Urban Scene Segmentation,
ECCV20(IX:695-714).
Springer DOI
2011
semi-supervised learning in unlabeled video sequences and extra images
to improve the performance on urban scene segmentation.
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
Jakubovitz, D.,
Rodrigues, M.R.D.,
Giryes, R.,
Lautum Regularization for Semi-Supervised Transfer Learning,
SDL-CV19(763-767)
IEEE DOI
2004
information theory, learning (artificial intelligence),
neural nets, source dataset, deep neural network, Information Theory
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 .