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IP(30), 2021, pp. 3793-3803.
IEEE DOI
2104
Correlation, Adaptation models, Feature extraction,
Target recognition, Data models, Transfer learning, Visualization,
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2107
Domain adaptation, Multi-source domain adaptation,
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Elsevier DOI
2109
Universal domain adaptation, Multi-source domain adaptation,
Universal multi-source domain adaptation,
Pseudo-margin vector
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Lasloum, T.[Tariq],
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Bazi, Y.[Yakoub],
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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],
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Deep CockTail Networks,
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Springer DOI
2108
BibRef
Xu, R.J.[Rui-Jia],
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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],
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Tao, J.W.[Jian-Wen],
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Elsevier DOI
2111
Multi-source adaptation, Domain generalization, Adaptive loss,
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A Two-Way alignment approach for unsupervised multi-Source domain
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PR(124), 2022, pp. 108430.
Elsevier DOI
2203
Domain adaptation, Feature extraction, Category prototype,
Adversarial training, Instance weighting
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Xiong, L.[Lin],
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Gan, Y.[Yan],
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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],
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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
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Wang, M.[Mengzhu],
Chen, D.[Dingyao],
Tan, F.Z.[Fang-Zhou],
Liang, T.Y.[Tian-Yi],
Lan, L.[Long],
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IET-CV(17), No. 1, 2023, pp. 26-38.
DOI Link
2303
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Shi, Y.C.[Yu-Cheng],
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Han, Y.[Yahong],
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Li, B.S.[Bing-Shuai],
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.Y.[Zi-Yun],
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
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Wu, L.[Lan],
Wang, H.[Han],
Yao, Y.[Yuan],
Multi-source to multi-target domain adaptation method based on
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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
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Wang, Y.Y.[Yun-Yun],
Mao, J.[Jian],
Zou, C.[Cong],
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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
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Chen, S.[Sentao],
Multi-Source Domain Adaptation with Mixture of Joint Distributions,
PR(149), 2024, pp. 110295.
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2403
Statistical machine learning, Multi-source domain adaptation,
Mixture joint distribution, Kernel method
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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
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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
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Yang, Y.X.[Yu-Xiang],
Hou, Y.[Yun],
Wen, L.[Lu],
Zeng, P.X.[Pin-Xian],
Wang, Y.[Yan],
Semantic-Aware Adaptive Prompt Learning for Universal Multi-Source
Domain Adaptation,
SPLetters(31), 2024, pp. 1444-1448.
IEEE DOI
2406
Semantics, Task analysis, Uncertainty, Prototypes, Vectors,
Feature extraction, Image classification, Domain adaptation, prompt learning
BibRef
Zhou, Y.D.[Yong-Duo],
Wang, C.[Cheng],
Zhang, H.B.[He-Bing],
Wang, H.T.[Hong-Tao],
Xi, X.H.[Xiao-Huan],
Yang, Z.[Zhou],
Du, M.[Meng],
TCPSNet: Transformer and Cross-Pseudo-Siamese Learning Network for
Classification of Multi-Source Remote Sensing Images,
RS(16), No. 17, 2024, pp. 3120.
DOI Link
2409
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
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
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 .