14.1.6.1 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.

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.[Xiaofu],
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

Rakshit, S.[Sayan], Mohanty, A.[Anwesh], Chavhan, R.[Ruchika], Banerjee, B.[Biplab], Roig, G.[Gemma], Chaudhuri, S.[Subhasis],
FRIDA: Generative feature replay for incremental domain adaptation,
CVIU(217), 2022, pp. 103367.
Elsevier DOI 2203
Domain adaptation, Incremental learning, Image classification, Adversarial learning BibRef

Rakshit, S.[Sayan], Tamboli, D.[Dipesh], Meshram, P.S.[Pragati Shuddhodhan], Banerjee, B.[Biplab], Roig, G.[Gemma], Chaudhuri, S.[Subhasis],
Multi-source Open-set Deep Adversarial Domain Adaptation,
ECCV20(XXVI:735-750).
Springer DOI 2011
BibRef

Rakshit, S.[Sayan], Banerjee, B.[Biplab], Roig, G.[Gemma], Chaudhuri, S.[Subhasis],
Unsupervised Multi-source Domain Adaptation Driven by Deep Adversarial Ensemble Learning,
GCPR19(485-498).
Springer DOI 1911
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


Li, R.H.[Rui-Huang], Jia, X.[Xu], He, J.Z.[Jian-Zhong], Chen, S.[Shuaijun], 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

Bucci, S.[Silvia], Borlino, F.C.[Francesco Cappio], Caputo, B.[Barbara], Tommasi, T.[Tatiana],
Distance-based Hyperspherical Classification for Multi-source Open-Set Domain Adaptation,
WACV22(1030-1039)
IEEE DOI 2202
Training, Adaptation models, Codes, Machine vision, Predictive models, Benchmark testing, Transfer, Few-shot, Semi- and Un- supervised Learning Object Detection/Recognition/Categorization 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.[Shuhao], 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

Li, D.[Da], Hospedales, T.M.[Timothy M.],
Online Meta-learning for Multi-source and Semi-supervised Domain Adaptation,
ECCV20(XVI: 382-403).
Springer DOI 2010
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
Unsupervised Domain Adaptation .


Last update:Jun 19, 2022 at 13:58:21