14.1.8.2 Multi-Source Domain Adaptation

Chapter Contents (Back)
Domain Adaptation. Multi-Source Adaptation. Some may be under:
See also Adversarial Networks for Transfer Learning, Domain Adaption.
See also Open-Set Domain Adaptation.

Karimpour, M.[Morvarid], Saray, S.N.[Shiva Noori], Tahmoresnezhad, J.[Jafar], Aghababa, M.P.[Mohammad Pourmahmood],
Multi-source domain adaptation for image classification,
MVA(31), No. 6, August 2020, pp. Article44.
Springer DOI 2008
BibRef

Niu, C.[Chang], Shang, J.[Junyuan], Zhou, Z.H.[Zhi-Heng], Huang, J.C.[Jun-Chu], Wang, T.L.[Tian-Lei], Li, X.W.[Xiang-Wei],
Common-specific feature learning for multi-source domain adaptation,
IET-IPR(14), No. 16, 19 December 2020, pp. 4049-4058.
DOI Link 2103
BibRef

Zuo, Y., Yao, H., Xu, C.,
Attention-Based Multi-Source Domain Adaptation,
IP(30), 2021, pp. 3793-3803.
IEEE DOI 2104
Correlation, Adaptation models, Feature extraction, Target recognition, Data models, Transfer learning, Visualization, weighted moment distance BibRef

Schrom, S.[Sebastian], Hasler, S.[Stephan], Adamy, J.[Jürgen],
Improved multi-source domain adaptation by preservation of factors,
IVC(112), 2021, pp. 104209.
Elsevier DOI 2107
Domain adaptation, Multi-source domain adaptation, Adversarial domain adaptation, Negative transfer, Domain factors BibRef

Yin, Y.M.[Yue-Ming], Yang, Z.[Zhen], Hu, H.F.[Hai-Feng], Wu, X.F.[Xiao-Fu],
Universal multi-Source domain adaptation for image classification,
PR(121), 2022, pp. 108238.
Elsevier DOI 2109
Universal domain adaptation, Multi-source domain adaptation, Universal multi-source domain adaptation, Pseudo-margin vector BibRef

Lasloum, T.[Tariq], Alhichri, H.[Haikel], Bazi, Y.[Yakoub], Alajlan, N.[Naif],
SSDAN: Multi-Source Semi-Supervised Domain Adaptation Network for Remote Sensing Scene Classification,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link 2110
BibRef

Chen, Z.L.[Zi-Liang], Wei, P.X.[Peng-Xu], Zhuang, J.Y.[Jing-Yu], Li, G.B.[Guan-Bin], Lin, L.[Liang],
Deep CockTail Networks,
IJCV(129), No. 8, August 2021, pp. 2328-2351.
Springer DOI 2108
BibRef

Xu, R.J.[Rui-Jia], Chen, Z.L.[Zi-Liang], Zuo, W.M.[Wang-Meng], Yan, J.J.[Jun-Jie], Lin, L.[Liang],
Deep CockTail Network: Multi-source Unsupervised Domain Adaptation with Category Shift,
CVPR18(3964-3973)
IEEE DOI 1812
Feature extraction, Adaptation models, Training, Protocols, Task analysis, Benchmark testing, Visualization BibRef

Wu, G.B.[Guang-Bin], Chen, W.S.[Wei-Shan], Zuo, W.M.[Wang-Meng], Zhang, D.[David],
Unsupervised Domain Adaptation with Robust Deep Logistic Regression,
PSIVT17(199-211).
Springer DOI 1802
BibRef

Tao, J.W.[Jian-Wen], Dan, Y.F.[Yu-Fang], Zhou, D.[Di],
Robust multi-source co-adaptation with adaptive loss minimization,
SP:IC(99), 2021, pp. 116455.
Elsevier DOI 2111
Multi-source adaptation, Domain generalization, Adaptive loss, Maximum mean discrepancy BibRef

Liu, Y.H.[Yong-Hui], Ren, C.X.[Chuan-Xian],
A Two-Way alignment approach for unsupervised multi-Source domain adaptation,
PR(124), 2022, pp. 108430.
Elsevier DOI 2203
Domain adaptation, Feature extraction, Category prototype, Adversarial training, Instance weighting BibRef

Xiong, L.[Lin], Ye, M.[Mao], Zhang, D.[Dan], Gan, Y.[Yan], Liu, Y.G.[Yi-Guang],
Source data-free domain adaptation for a faster R-CNN,
PR(124), 2022, pp. 108436.
Elsevier DOI 2203
Source data-free, Object detection, Domain adaptation, Transfer learning BibRef

Liu, J.[Jia], Xuan, W.J.[Wen-Jie], Gan, Y.H.[Yu-Hang], Zhan, Y.B.[Yi-Bing], Liu, J.[Juhua], Du, B.[Bo],
An End-to-end Supervised Domain Adaptation Framework for Cross-Domain Change Detection,
PR(132), 2022, pp. 108960.
Elsevier DOI 2209
Change Detection, Supervised Domain Adaptation, Image Adaptation, Feature Adaptation BibRef

Wang, M.[Mengzhu], Chen, D.[Dingyao], Tan, F.Z.[Fang-Zhou], Liang, T.Y.[Tian-Yi], Lan, L.[Long], Zhang, X.[Xiang], Luo, Z.G.[Zhi-Gang],
Domain-specific feature recalibration and alignment for multi-source unsupervised domain adaptation,
IET-CV(17), No. 1, 2023, pp. 26-38.
DOI Link 2303
BibRef

Shi, Y.C.[Yu-Cheng], Wu, K.[Kunhong], Han, Y.[Yahong], Shao, Y.F.[Yun-Feng], Li, B.[Bingshuai], Wu, F.[Fei],
Source-free and black-box domain adaptation via distributionally adversarial training,
PR(143), 2023, pp. 109750.
Elsevier DOI 2310
Source-free unsupervised domain adaptation, Distributionally adversarial training, Black-box probe BibRef

Cai, Z.[Ziyun], Zhang, D.D.[Dan-Dan], Zhang, T.F.[Teng-Fei], Hu, C.H.[Chang-Hui], Jing, X.Y.[Xiao-Yuan],
Single-/Multi-Source Domain Adaptation via domain separation: A simple but effective method,
PRL(174), 2023, pp. 124-129.
Elsevier DOI 2310
Contrastive Classifier Network (CCN), Domain-specific Learning (DSLN), Domain Adaptation (DA) BibRef

Liu, X.H.[Xin-Hui], Xi, W.[Wei], Li, W.[Wen], Xu, D.[Dong], Bai, G.[Gairui], Zhao, J.Z.[Ji-Zhong],
Co-MDA: Federated Multisource Domain Adaptation on Black-Box Models,
CirSysVideo(33), No. 12, December 2023, pp. 7658-7670.
IEEE DOI 2312
BibRef

Wu, L.[Lan], Wang, H.[Han], Yao, Y.[Yuan],
Multi-source to multi-target domain adaptation method based on similarity measurement,
IET-IPR(18), No. 1, 2024, pp. 34-46.
DOI Link 2401
cross-domain mutual learning strategy, distributed alignment, domain adaptation, image classification, similarity measurement BibRef

Xu, M.H.[Ming-Hao], Wang, H.[Hang], Ni, B.B.[Bing-Bing],
Graphical Modeling for Multi-Source Domain Adaptation,
PAMI(46), No. 3, March 2024, pp. 1727-1741.
IEEE DOI 2402
Adaptation models, Prototypes, Markov random fields, Semantics, Predictive models, Graphical models, Computational modeling, markov random field BibRef

Wang, Y.Y.[Yun-Yun], Mao, J.[Jian], Zou, C.[Cong], Kong, X.Y.[Xin-Yang],
Universal domain adaptation from multiple black-box sources,
IVC(142), 2024, pp. 104896.
Elsevier DOI 2402
Unsupervised domain adaptation, Universal domain adaptation, Multiple black-box sources, Domain attention, Class attention BibRef

Chen, S.[Sentao],
Multi-Source Domain Adaptation with Mixture of Joint Distributions,
PR(149), 2024, pp. 110295.
Elsevier DOI Code:
WWW Link. 2403
Statistical machine learning, Multi-source domain adaptation, Mixture joint distribution, Kernel method BibRef

Lu, Y.[Yuwu], Huang, H.Y.[Hao-Yu], Zeng, B.Q.[Bi-Qing], Lai, Z.H.[Zhi-Hui], Li, X.L.[Xue-Long],
Multi-Source and Multi-Target Domain Adaptation Based on Dynamic Generator with Attention,
MultMed(26), 2024, pp. 6891-6905.
IEEE DOI 2405
Generators, Feature extraction, Adaptation models, Training, Task analysis, Adversarial machine learning, Semantics, attention mechanism BibRef

Wong, W.K.[Wai Keung], Lu, Y.[Yuwu], Lai, Z.H.[Zhi-Hui], Li, X.L.[Xue-Long],
Graph correlated discriminant embedding for multi-source domain adaptation,
PR(153), 2024, pp. 110538.
Elsevier DOI 2405
Multi-source domain adaptation, Graph embedding, Discriminative, Correlative BibRef


Huang, C.J.[Cheng-Jie], Abdelzad, V.[Vahdat], Sedwards, S.[Sean], Czarnecki, K.[Krzysztof],
SOAP: Cross-sensor Domain Adaptation for 3D Object Detection Using Stationary Object Aggregation Pseudo-labelling,
WACV24(3340-3349)
IEEE DOI 2404
Training, Point cloud compression, Adaptation models, Aggregates, Object detection, Algorithms, 3D computer vision, Applications, Autonomous Driving BibRef

Zhang, L.[Lin], Xu, L.H.[Ling-Han], Motamed, S.[Saman], Chakraborty, S.[Shayok], de la Torre, F.[Fernando],
D3GU: Multi-target Active Domain Adaptation via Enhancing Domain Alignment,
WACV24(2565-2574)
IEEE DOI 2404
Training, Art, Image recognition, Annotations, Image sampling, Benchmark testing, Algorithms, Machine learning architectures BibRef

Takeda, K.[Koji], Tanaka, K.[Kanji], Nakamura, Y.[Yoshimasa],
Lifelong Change Detection: Continuous Domain Adaptation for Small Object Change Detection in Everyday Robot Navigation,
MVA23(1-5)
DOI Link 2403
Visualization, Uncertainty, Navigation, Filtering, Machine vision, Supervised learning, Manuals BibRef

Girdhar, R.[Rohit], El-Nouby, A.[Alaaeldin], Liu, Z.[Zhuang], Singh, M.[Mannat], Alwala, K.V.[Kalyan Vasudev], Joulin, A.[Armand], Misra, I.[Ishan],
ImageBind One Embedding Space to Bind Them All,
CVPR23(15180-15190)
IEEE DOI 2309
Multimodal data. BibRef

Lee, Y.L.[Yi-Lun], Tsai, Y.H.[Yi-Hsuan], Chiu, W.C.[Wei-Chen], Lee, C.Y.[Chen-Yu],
Multimodal Prompting with Missing Modalities for Visual Recognition,
CVPR23(14943-14952)
IEEE DOI 2309

WWW Link. BibRef

Ge, Y.H.[Yun-Hao], Ren, J.[Jie], Gallagher, A.[Andrew], Wang, Y.X.[Yu-Xiao], Yang, M.H.[Ming-Hsuan], Adam, H.[Hartwig], Itti, L.[Laurent], Lakshminarayanan, B.[Balaji], Zhao, J.[Jiaping],
Improving Zero-shot Generalization and Robustness of Multi-Modal Models,
CVPR23(11093-11101)
IEEE DOI 2309
BibRef

Shin, H.[Hyungseob], Kim, H.[Hyeongyu], Kim, S.[Sewon], Jun, Y.[Yohan], Eo, T.[Taejoon], Hwang, D.[Dosik],
SDC-UDA: Volumetric Unsupervised Domain Adaptation Framework for Slice-Direction Continuous Cross-Modality Medical Image Segmentation,
CVPR23(7412-7421)
IEEE DOI 2309
BibRef

Kumar, V.[Vikash], Lal, R.[Rohit], Patil, H.[Himanshu], Chakraborty, A.[Anirban],
CoNMix for Source-free Single and Multi-target Domain Adaptation,
WACV23(4167-4177)
IEEE DOI 2302
Adaptation models, Uncertainty, Heuristic algorithms, Feature extraction, Transformers, Noise measurement, Vision + language and/or other modalities BibRef

Wang, J.[Jie], Zhong, C.L.[Chao-Liang], Feng, C.[Cheng], Zhang, Y.[Ying], Sun, J.[Jun], Yokota, Y.[Yasuto],
Discriminative Mutual Learning for Multi-Target Domain Adaptation,
ICPR22(2900-2906)
IEEE DOI 2212
Learning systems, Adaptation models, Predictive models, Benchmark testing, Data models BibRef

Xia, H.F.[Hai-Feng], Wang, P.[Pu], Ding, Z.M.[Zheng-Ming],
Incomplete Multi-view Domain Adaptation via Channel Enhancement and Knowledge Transfer,
ECCV22(XXXIV:200-217).
Springer DOI 2211
BibRef

Peng, X.K.[Xiao-Kang], Wei, Y.[Yake], Deng, A.D.[An-Dong], Wang, D.[Dong], Hu, D.[Di],
Balanced Multimodal Learning via On-the-fly Gradient Modulation,
CVPR22(8228-8237)
IEEE DOI 2210
Adaptation models, Codes, Gaussian noise, Modulation, Pattern recognition, Task analysis, Vision+X, Video analysis and understanding BibRef

Li, R.H.[Rui-Huang], Jia, X.[Xu], He, J.Z.[Jian-Zhong], Chen, S.J.[Shuai-Jun], Hu, Q.H.[Qing-Hua],
T-SVDNet: Exploring High-Order Prototypical Correlations for Multi-Source Domain Adaptation,
ICCV21(9971-9980)
IEEE DOI 2203
Training, Adaptation models, Tensors, Correlation, Benchmark testing, Data structures, Matrix decomposition, Representation learning, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Gong, R.[Rui], Dai, D.X.[Deng-Xin], Chen, Y.H.[Yu-Hua], Li, W.[Wen], Van Gool, L.J.[Luc J.],
mDALU: Multi-Source Domain Adaptation and Label Unification with Partial Datasets,
ICCV21(8856-8865)
IEEE DOI 2203
Image segmentation, Uncertainty, Annotations, Semantics, Benchmark testing, Object recognition, grouping and shape BibRef

Gong, R.[Rui], Li, W.[Wen], Chen, Y.H.[Yu-Hua], Van Gool, L.J.[Luc J.],
DLOW: Domain Flow for Adaptation and Generalization,
CVPR19(2472-2481).
IEEE DOI 2002
BibRef

Park, G.Y.[Geon Yeong], Lee, S.W.[Sang Wan],
Information-theoretic regularization for Multi-source Domain Adaptation,
ICCV21(9194-9203)
IEEE DOI 2203
Training, Image synthesis, Scalability, Computer network reliability, Computational modeling, Adversarial learning BibRef

Xu, Y.Y.[Yuan-Yuan], Kan, M.[Meina], Shan, S.G.[Shi-Guang], Chen, X.L.[Xi-Lin],
Mutual Learning of Joint and Separate Domain Alignments for Multi-Source Domain Adaptation,
WACV22(1658-1667)
IEEE DOI 2202
Diversity reception, Task analysis, Transfer, Few-shot, Semi- and Un- supervised Learning BibRef

Amosy, O.[Ohad], Chechik, G.[Gal],
Coupled Training for Multi-Source Domain Adaptation,
WACV22(1071-1080)
IEEE DOI 2202
Training, Couplings, Adaptation models, Analytical models, Benchmark testing, Predictive models, Transfer, Semi- and Un- supervised Learning BibRef

Nguyen, V.A.[Van-Anh], Nguyen, T.[Tuan], Le, T.[Trung], Tran, Q.H.[Quan Hung], Phung, D.[Dinh],
STEM: An approach to Multi-source Domain Adaptation with Guarantees,
ICCV21(9332-9343)
IEEE DOI 2203
Adaptation models, Soft sensors, Computational modeling, Benchmark testing, Feature extraction, Generators, BibRef

Qiu, S.H.[Shu-Hao], Zhu, C.[Chuang], Zhou, W.L.[Wen-Li],
Meta Self-Learning for Multi-Source Domain Adaptation: A Benchmark,
ILDAV21(1592-1601)
IEEE DOI 2112
Learning systems, Deep learning, Text recognition, Computational modeling, Training data BibRef

Montesuma, E.F.[Eduardo Fernandes], Mboula, F.M.N.[Fred Maurice Ngolè],
Wasserstein Barycenter for Multi-Source Domain Adaptation,
CVPR21(16780-16788)
IEEE DOI 2111
Visualization, Adaptation models, Face recognition, Probability distribution, Data models, Acoustics BibRef

Fu, Y.Y.[Yang-Ye], Zhang, M.[Ming], Xu, X.[Xing], Cao, Z.[Zuo], Ma, C.[Chao], Ji, Y.L.[Yan-Li], Zuo, K.[Kai], Lu, H.M.[Hui-Min],
Partial Feature Selection and Alignment for Multi-Source Domain Adaptation,
CVPR21(16649-16658)
IEEE DOI 2111
Adaptation models, Correlation, Handheld computers, Computational modeling, Benchmark testing, Feature extraction BibRef

Li, Y.S.[Yun-Sheng], Yuan, L.[Lu], Chen, Y.P.[Yin-Peng], Wang, P.[Pei], Vasconcelos, N.M.[Nuno M.],
Dynamic Transfer for Multi-Source Domain Adaptation,
CVPR21(10993-11002)
IEEE DOI 2111
Degradation, Adaptation models, Codes, Convolution, Pattern recognition BibRef

Wang, H.[Hang], Xu, M.H.[Ming-Hao], Ni, B.B.[Bing-Bing], Zhang, W.J.[Wen-Jun],
Learning to Combine: Knowledge Aggregation for Multi-source Domain Adaptation,
ECCV20(VIII:727-744).
Springer DOI 2011
BibRef

Yang, L.[Luyu], Balaji, Y.[Yogesh], Lim, S.N.[Ser-Nam], Shrivastava, A.[Abhinav],
Curriculum Manager for Source Selection in Multi-source Domain Adaptation,
ECCV20(XIV:608-624).
Springer DOI 2011
BibRef

Yi, H.Y.[Hai-Yang], Xu, Z.[Zhi], Wen, Y.M.[Yi-Min], Fan, Z.G.[Zhi-Gang],
Multi-source Domain Adaptation for Face Recognition,
ICPR18(1349-1354)
IEEE DOI 1812
Face recognition, Image reconstruction, Correlation, Optimization, Feature extraction, Sparse matrices, Training, domain adaptation, face recognition BibRef

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


Last update:May 29, 2024 at 17:34:46