14.1.6.1 Unsupervised Domain Adaptation

Chapter Contents (Back)
Unsupervised Adaptation. Transfer Learning. Domain Adaptation.
See also Transfer Learning from Other Classes.
See also Multi-Label Classification, Multilabel Classification.
See also Knowledge Distillation.

Gong, B.Q.[Bo-Qing], Grauman, K.[Kristen], Sha, F.[Fei],
Learning Kernels for Unsupervised Domain Adaptation with Applications to Visual Object Recognition,
IJCV(109), No. 1-2, August 2014, pp. 3-27.
Springer DOI 1407
Correct mismatch between source and target domain. BibRef

Gong, B.Q.[Bo-Qing], Shi, Y.[Yuan], Sha, F.[Fei], Grauman, K.[Kristen],
Geodesic flow kernel for unsupervised domain adaptation,
CVPR12(2066-2073).
IEEE DOI 1208
BibRef

Gopalan, R.[Raghuraman], Li, R.N.[Ruo-Nan], Chellappa, R.[Rama],
Unsupervised Adaptation Across Domain Shifts by Generating Intermediate Data Representations,
PAMI(36), No. 11, November 2014, pp. 2288-2302.
IEEE DOI 1410
BibRef
Earlier:
Domain adaptation for object recognition: An unsupervised approach,
ICCV11(999-1006).
IEEE DOI 1201
Adaptation models. Adapting based on training on different domain. BibRef

Li, R.N.[Ruo-Nan], Patel, V.M.[Vishal M.], Gopalan, R.[Raghuraman], Chellappa, R.[Rama],
Domain Adaptation for Visual Recognition,
FTCGV(8), No. 4, 2015, pp. 285-378.
DOI Link 1503
BibRef

Patel, V.M.[Vishal M.], Gopalan, R.[Raghuraman], Li, R.N.[Ruo-Nan], Chellappa, R.,
Visual Domain Adaptation: A survey of recent advances,
SPMag(32), No. 3, May 2015, pp. 53-69.
IEEE DOI 1504
Classification algorithms BibRef

Samanta, S., Das, S.,
Unsupervised domain adaptation using eigenanalysis in kernel space for categorisation tasks,
IET-IPR(9), No. 11, 2015, pp. 925-930.
DOI Link 1511
Hilbert spaces BibRef

Selvan, A.T., Samanta, S., Das, S.,
Domain adaptation using weighted sub-space sampling for object categorization,
ICAPR15(1-5)
IEEE DOI 1511
differential geometry BibRef

Fernando, B.[Basura], Tommasi, T.[Tatiana], Tuytelaars, T.[Tinne],
Joint cross-domain classification and subspace learning for unsupervised adaptation,
PRL(65), No. 1, 2015, pp. 60-66.
Elsevier DOI 1511
BibRef
Earlier: A2, A3, Only:
A Testbed for Cross-Dataset Analysis,
TASKCV14(18-31).
Springer DOI 1504
Unsupervised domain adaptation BibRef

Redko, I.[Ievgen], Bennani, Y.[Younès],
Non-negative embedding for fully unsupervised domain adaptation,
PRL(77), No. 1, 2016, pp. 35-41.
Elsevier DOI 1606
BibRef
And:
Kernel alignment for unsupervised transfer learning,
ICPR16(525-530)
IEEE DOI 1705
Bridges, Indexes, Kernel, Optimization, Partitioning algorithms, Standards, Symmetric matrices. Transfer learning BibRef

Zhu, L.[Lei], Ma, L.[Li],
Class centroid alignment based domain adaptation for classification of remote sensing images,
PRL(83, Part 2), No. 1, 2016, pp. 124-132.
Elsevier DOI 1609
Domain adaptation BibRef

Ma, L.[Li], Crawford, M.M.[Melba M.], Zhu, L.[Lei], Liu, Y.[Yong],
Centroid and Covariance Alignment-Based Domain Adaptation for Unsupervised Classification of Remote Sensing Images,
GeoRS(57), No. 4, April 2019, pp. 2305-2323.
IEEE DOI 1904
geophysical image processing, image classification, image filtering, remote sensing, spatial filters, remote sensing BibRef

Liu, Z.X.[Zi-Xu], Ma, L.[Li], Du, Q.[Qian],
Class-Wise Distribution Adaptation for Unsupervised Classification of Hyperspectral Remote Sensing Images,
GeoRS(59), No. 1, January 2021, pp. 508-521.
IEEE DOI 2012
Feature extraction, Hyperspectral imaging, Neural networks, Generative adversarial networks, remote sensing BibRef

Hou, C.A.[Cheng-An], Tsai, Y.H.H.[Yao-Hung Hubert], Yeh, Y.R.[Yi-Ren], Wang, Y.C.A.F.[Yu-Chi-Ang Frank],
Unsupervised Domain Adaptation With Label and Structural Consistency,
IP(25), No. 12, December 2016, pp. 5552-5562.
IEEE DOI 1612
BibRef
Earlier: A2, A3, A4, Only:
Learning Cross-Domain Landmarks for Heterogeneous Domain Adaptation,
CVPR16(5081-5090)
IEEE DOI 1612
pattern classification BibRef

Chen, W.Y.[Wei-Yu], Hsu, T.M.H.[Tzu-Ming Harry], Tsai, Y.H.H.[Yao-Hung Hubert], Wang, Y.C.A.F.[Yu-Chi-Ang Frank], Chen, M.S.[Ming-Syan],
Transfer Neural Trees for Heterogeneous Domain Adaptation,
ECCV16(V: 399-414).
Springer DOI 1611
BibRef

Hsu, T.M.H.[Tzu-Ming Harry], Chen, W.Y.[Wei-Yu], Hou, C.A.[Cheng-An], Tsai, Y.H.H., Yeh, Y.R.[Yi-Ren], Wang, Y.C.A.F.[Yu-Chi-Ang Frank],
Unsupervised Domain Adaptation with Imbalanced Cross-Domain Data,
ICCV15(4121-4129)
IEEE DOI 1602
Computer vision BibRef

Chen, W.Y.[Wei-Yu], Hsu, T.M.H.[Tzu-Ming Harry], Hou, C.A.[Cheng-An], Yeh, Y.R.[Yi-Ren], Wang, Y.C.A.F.[Yu-Chi-Ang Frank],
Connecting the dots without clues: Unsupervised domain adaptation for cross-domain visual classification,
ICIP15(3997-4001)
IEEE DOI 1512
BibRef
Earlier: A3, A4, A5, Only:
An unsupervised domain adaptation approach for cross-domain visual classification,
AVSS15(1-6)
IEEE DOI 1511
Unsupervised domain adaptation motion estimation BibRef

Chou, Y.C.[Yen-Cheng], Wei, C.P.[Chia-Po], Wang, Y.C.A.F.[Yu-Chi-Ang Frank],
A discriminative domain adaptation model for cross-domain image classification,
ICIP13(3083-3087)
IEEE DOI 1402
Domain adaptation; image classification; low-rank matrix decomposition BibRef

Li, C.G.[Chun-Guang], You, C., Vidal, R.[Rene],
Structured Sparse Subspace Clustering: A Joint Affinity Learning and Subspace Clustering Framework,
IP(26), No. 6, June 2017, pp. 2988-3001.
IEEE DOI 1705
BibRef
Earlier: A1, A3, Only:
Structured Sparse Subspace Clustering: A unified optimization framework,
CVPR15(277-286)
IEEE DOI 1510
Cancer, Computer vision, Face, Motion segmentation, Optimization, Sparse matrices, Videos, Structured sparse subspace clustering, cancer clustering, constrained subspace clustering, structured subspace clustering, subspace, structured, norm BibRef

Patel, V.M.[Vishal M.], Vidal, R.[Rene],
Kernel sparse subspace clustering,
ICIP14(2849-2853)
IEEE DOI 1502
Clustering algorithms BibRef

Shrivastava, A.[Ashish], Shekhar, S.[Sumit], Patel, V.M.[Vishal M.],
Unsupervised domain adaptation using parallel transport on Grassmann manifold,
WACV14(277-284)
IEEE DOI 1406
Clustering algorithms BibRef

Venkateswara, H.[Hemanth], Chakraborty, S.[Shayok], Panchanathan, S.[Sethuraman],
Deep-Learning Systems for Domain Adaptation in Computer Vision: Learning Transferable Feature Representations,
SPMag(34), No. 6, November 2017, pp. 117-129.
IEEE DOI 1712
BibRef
Earlier:
Nonlinear Embedding Transform for Unsupervised Domain Adaptation,
TASKCV16(III: 451-457).
Springer DOI 1611
Adaptation models, Data models, Knowledge transfer, Machine learning, Training data BibRef

Ranganathan, H., Venkateswara, H.[Hemanth], Chakraborty, S.[Shayok], Panchanathan, S.[Sethuraman],
Deep active learning for image classification,
ICIP17(3934-3938)
IEEE DOI 1803
Computational modeling, Computer vision, Entropy, Labeling, Machine learning, Training, Uncertainty, Computer vision, entropy BibRef

Venkateswara, H.[Hemanth], Eusebio, J., Chakraborty, S.[Shayok], Panchanathan, S.[Sethuraman],
Deep Hashing Network for Unsupervised Domain Adaptation,
CVPR17(5385-5394)
IEEE DOI 1711
Adaptation models, Data models, Machine learning, Neural networks, Training BibRef

Paris, C., Bruzzone, L.,
A Novel Approach to the Unsupervised Extraction of Reliable Training Samples From Thematic Products,
GeoRS(59), No. 3, March 2021, pp. 1930-1948.
IEEE DOI 2103
Training, Reliability, Databases, Semantics, Data mining, Spatial resolution, Remote sensing, Land-cover map update, weak learning classification BibRef

Lu, H., Shen, C., Cao, Z., Xiao, Y., van den Hengel, A.,
An Embarrassingly Simple Approach to Visual Domain Adaptation,
IP(27), No. 7, July 2018, pp. 3403-3417.
IEEE DOI 1805
Adaptation models, Closed-form solutions, Iterative methods, Robustness, Training, Training data, Visualization, scene classification BibRef

Lu, H., Zhang, L., Cao, Z., Wei, W., Xian, K., Shen, C., van den Hengel, A.,
When Unsupervised Domain Adaptation Meets Tensor Representations,
ICCV17(599-608)
IEEE DOI 1802
image representation, learning (artificial intelligence), matrix algebra, tensors, Visualization BibRef

Yang, B.Y.[Bao-Yao], Ma, A.J.[Andy J.], Yuen, P.C.[Pong C.],
Learning domain-shared group-sparse representation for unsupervised domain adaptation,
PR(81), 2018, pp. 615-632.
Elsevier DOI 1806
Domain adaptation, Dictionary learning BibRef

Yang, B.Y.[Bao-Yao], Yuen, P.C.[Pong C.],
Learning adaptive geometry for unsupervised domain adaptation,
PR(110), 2021, pp. 107638.
Elsevier DOI 2011
Domain adaptation, Manifold structure, Distribution alignment BibRef

Chen, Y.[Yu], Yang, C.L.[Chun-Ling], Zhang, Y.[Yan],
Deep domain similarity Adaptation Networks for across domain classification,
PRL(112), 2018, pp. 270-276.
Elsevier DOI 1809
Deep learning, Domain adaptation, Domain similarity, Image classification BibRef

Chen, Y.[Yu], Yang, C.L.[Chun-Ling], Zhang, Y.[Yan], Li, Y.[Yuze],
Deep conditional adaptation networks and label correlation transfer for unsupervised domain adaptation,
PR(98), 2020, pp. 107072.
Elsevier DOI 1911
Conditional domain adaptation, Deep learning, Unsupervised learning, Label transfer BibRef

Huang, J.[Junchu], Zhou, Z.H.[Zhi-Heng],
Transfer metric learning for unsupervised domain adaptation,
IET-IPR(13), No. 5, 18 April 2019, pp. 804-810.
DOI Link 1904
BibRef

Zhang, L., Wang, P., Wei, W., Lu, H., Shen, C., van den Hengel, A., Zhang, Y.,
Unsupervised Domain Adaptation Using Robust Class-Wise Matching,
CirSysVideo(29), No. 5, May 2019, pp. 1339-1349.
IEEE DOI 1905
Robustness, Image color analysis, Visualization, Data models, Computer science, Australia, Adaptation models, unsupervised domain adaptation BibRef

Le, T.N.[Tien-Nam], Habrard, A.[Amaury], Sebban, M.[Marc],
Deep multi-Wasserstein unsupervised domain adaptation,
PRL(125), 2019, pp. 249-255.
Elsevier DOI 1909
Domain adaptation, Deep learning, Wasserstein metric, Optimal transport BibRef

Liang, J.[Jian], He, R.[Ran], Sun, Z.A.[Zhen-An], Tan, T.N.[Tie-Niu],
Exploring uncertainty in pseudo-label guided unsupervised domain adaptation,
PR(96), 2019, pp. 106996.
Elsevier DOI 1909
Unsupervised domain adaptation, Pseudo labeling, Feature transformation, Progressive learning, Transfer learning BibRef

Damodaran, B.B.[Bharath Bhushan], Flamary, R.[Rémi], Seguy, V.[Vivien], Courty, N.[Nicolas],
An Entropic Optimal Transport loss for learning deep neural networks under label noise in remote sensing images,
CVIU(191), 2020, pp. 102863.
Elsevier DOI 2002
Optimal transport, Entropic Optimal Transport, Robust deep learning, Noisy labels, Remote sensing BibRef

Damodaran, B.B.[Bharath Bhushan], Kellenberger, B.[Benjamin], Flamary, R.[Rémi], Tuia, D.[Devis], Courty, N.[Nicolas],
DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation,
ECCV18(II: 467-483).
Springer DOI 1810
BibRef

Li, R., Cao, W., Wu, S., Wong, H.,
Generating Target Image-Label Pairs for Unsupervised Domain Adaptation,
IP(29), 2020, pp. 7997-8011.
IEEE DOI 2008
Semantics, Adaptation models, Task analysis, Image segmentation, Data models, Feature extraction, image generation BibRef

Yan, H.L.[Hong-Liang], Li, Z.T.[Zhe-Tao], Wang, Q.L.[Qi-Long], Li, P.H.[Pei-Hua], Xu, Y.[Yong], Zuo, W.M.[Wang-Meng],
Weighted and Class-Specific Maximum Mean Discrepancy for Unsupervised Domain Adaptation,
MultMed(22), No. 9, September 2020, pp. 2420-2433.
IEEE DOI 2008
Measurement, Adaptation models, Airplanes, Task analysis, Generative adversarial networks, Degradation, expectation-maximization algorithms BibRef

Yan, H.L.[Hong-Liang], Ding, Y.K.[Yu-Kang], Li, P.H.[Pei-Hua], Wang, Q.L.[Qi-Long], Xu, Y.[Yong], Zuo, W.M.[Wang-Meng],
Mind the Class Weight Bias: Weighted Maximum Mean Discrepancy for Unsupervised Domain Adaptation,
CVPR17(945-954)
IEEE DOI 1711
Adaptation models, Computational modeling, Kernel, Manganese, Measurement, Training BibRef

Luo, L., Chen, L., Hu, S., Lu, Y., Wang, X.,
Discriminative and Geometry-Aware Unsupervised Domain Adaptation,
Cyber(50), No. 9, September 2020, pp. 3914-3927.
IEEE DOI 2008
Data models, Manifolds, Task analysis, Training, Benchmark testing, Analytical models, Labeling, Data distribution matching, visual classification BibRef

Zuo, L.[Lin], Jing, M.M.[Meng-Meng], Li, J.J.[Jing-Jing], Zhu, L.[Lei], Lu, K.[Ke], Yang, Y.[Yang],
Challenging tough samples in unsupervised domain adaptation,
PR(110), 2021, pp. 107540.
Elsevier DOI 2011
Domain adaptation, transfer learning, adversarial learning BibRef

Xu, X., He, H., Zhang, H., Xu, Y., He, S.,
Unsupervised Domain Adaptation via Importance Sampling,
CirSysVideo(30), No. 12, December 2020, pp. 4688-4699.
IEEE DOI 2012
Feature extraction, Entropy, Tuning, Estimation, Monte Carlo methods, Adaptation models, Noise measurement, Domain adaptation, distribution sampling BibRef

Wang, X.M.[Xing-Mei], Sun, B.X.[Bo-Xuan], Dong, H.B.[Hong-Bin],
Domain-invariant adversarial learning with conditional distribution alignment for unsupervised domain adaptation,
IET-CV(14), No. 8, December 2020, pp. 642-649.
DOI Link 2012
BibRef

Madadi, Y.[Yeganeh], Seydi, V.[Vahid], Nasrollahi, K.[Kamal], Hosseini, R.[Reshad], Moeslund, T.B.[Thomas B.],
Deep visual unsupervised domain adaptation for classification tasks: A survey,
IET-IPR(14), No. 14, December 2020, pp. 3283-3299.
DOI Link 2012
Survey, Domain Adaption. BibRef

Mancini, M.[Massimiliano], Porzi, L.[Lorenzo], Buló, S.R.[Samuel Rota], Caputo, B.[Barbara], Ricci, E.[Elisa],
Inferring Latent Domains for Unsupervised Deep Domain Adaptation,
PAMI(43), No. 2, February 2021, pp. 485-498.
IEEE DOI 2101
BibRef
Earlier:
Boosting Domain Adaptation by Discovering Latent Domains,
CVPR18(3771-3780)
IEEE DOI 1812
Adaptation models, Data models, Computer architecture, Neural networks, Training, Visualization, Training data, object recognition. Data models, Computer architecture, Training BibRef

Mancini, M.[Massimiliano], Porzi, L.[Lorenzo], Cermelli, F.[Fabio], Caputo, B.[Barbara],
Discovering Latent Domains for Unsupervised Domain Adaptation Through Consistency,
CIAP19(II:390-401).
Springer DOI 1909
BibRef

Li, H.L.[Hao-Liang], Wan, R.J.[Ren-Jie], Wang, S.Q.[Shi-Qi], Kot, A.C.[Alex C.],
Unsupervised Domain Adaptation in the Wild via Disentangling Representation Learning,
IJCV(129), No. 2, February 2021, pp. 267-283.
Springer DOI 2102
BibRef

Han, C.[Chao], Zhou, D.[Deyun], Xie, Y.[Yu], Gong, M.[Maoguo], Lei, Y.[Yu], Shi, J.[Jiao],
Collaborative representation with curriculum classifier boosting for unsupervised domain adaptation,
PR(113), 2021, pp. 107802.
Elsevier DOI 2103
Domain adaptation, Collaborative representation, Curriculum learning, Classifier boosting BibRef

Zhou, Q.[Qiang], Zhou, W.[Wen'an], Wang, S.R.[Shi-Rui],
Cluster adaptation networks for unsupervised domain adaptation,
IVC(108), 2021, pp. 104137.
Elsevier DOI 2104
Domain adaptation, Deep networks, Image classification BibRef

Chen, C.[Chao], Fu, Z.H.[Zhi-Hang], Chen, Z.H.[Zhi-Hong], Cheng, Z.W.[Zhao-Wei], Jin, X.Y.[Xin-Yu],
Towards self-similarity consistency and feature discrimination for unsupervised domain adaptation,
SP:IC(94), 2021, pp. 116232.
Elsevier DOI 2104
Domain adaptation, Self-similarity consistency, Feature discrimination, Intra-class compactness, Inter-class separability BibRef

Tang, H.[Hui], Jia, K.[Kui],
Vicinal and categorical domain adaptation,
PR(115), 2021, pp. 107907.
Elsevier DOI 2104
Unsupervised domain adaptation, Categorical domain adaptation, Vicinal domain adaptation, Cross-domain weighting, Domain augmentation BibRef

Wang, J.[Jing], Chen, J.[Jiahong], Lin, J.Z.[Jian-Zhe], Sigal, L.[Leonid], de Silva, C.W.[Clarence W.],
Discriminative feature alignment: Improving transferability of unsupervised domain adaptation by Gaussian-guided latent alignment,
PR(116), 2021, pp. 107943.
Elsevier DOI 2106
Domain adaptation, Information theory BibRef

Li, S.[Shuang], Liu, C.H.[Chi Harold], Lin, Q.X.[Qiu-Xia], Wen, Q.[Qi], Su, L.M.[Li-Min], Huang, G.[Gao], Ding, Z.M.[Zheng-Ming],
Deep Residual Correction Network for Partial Domain Adaptation,
PAMI(43), No. 7, July 2021, pp. 2329-2344.
IEEE DOI 2106
Task analysis, Deep learning, Visualization, Learning systems, Training, Probability distribution, Measurement, fine-grained visual recognition BibRef

Ding, Z.M.[Zheng-Ming], Li, S.[Sheng], Shao, M.[Ming], Fu, Y.[Yun],
Graph Adaptive Knowledge Transfer for Unsupervised Domain Adaptation,
ECCV18(II: 36-52).
Springer DOI 1810
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

Deng, W.X.[Wan-Xia], Liao, Q.[Qing], Zhao, L.J.[Ling-Jun], Guo, D.[Deke], Kuang, G.Y.[Gang-Yao], Hu, D.[Dewen], Liu, L.[Li],
Joint Clustering and Discriminative Feature Alignment for Unsupervised Domain Adaptation,
IP(30), 2021, pp. 7842-7855.
IEEE DOI 2109
Feature extraction, Task analysis, Image reconstruction, Training, Image coding, Deep learning, Data mining, Domain adaptation, semisupervised learning BibRef


Zhang, Y.[Youshan], Davison, B.D.[Brian D.],
Efficient Pre-trained Features and Recurrent Pseudo-Labeling in Unsupervised Domain Adaptation,
LLID21(2713-2722)
IEEE DOI 2109
Training, Adaptation models, Computational modeling, Benchmark testing BibRef

Singh, A.[Anurag], Doraiswamy, N.[Naren], Takamuku, S.[Sawa], Bhalerao, M.[Megh], Dutta, T.[Titir], Biswas, S.[Soma], Chepuri, A.[Aditya], Vengatesan, B.[Balasubramanian], Natori, N.[Naotake],
Improving Semi-Supervised Domain Adaptation Using Effective Target Selection and Semantics,
LLID21(2703-2712)
IEEE DOI 2109
Uncertainty, Computational modeling, Semantics, Prototypes, Manuals BibRef

Chao, C.H.[Chen-Hao], Cheng, B.W.[Bo-Wun], Lee, C.Y.[Chun-Yi],
Rethinking Ensemble-Distillation for Semantic Segmentation Based Unsupervised Domain Adaption,
LLID21(2610-2620)
IEEE DOI 2109
Learning systems, Semantics, Benchmark testing, Robustness, Pattern recognition BibRef

Barbato, F.[Francesco], Toldo, M.[Marco], Michieli, U.[Umberto], Zanuttigh, P.[Pietro],
Latent Space Regularization for Unsupervised Domain Adaptation in Semantic Segmentation,
WAD21(2829-2839)
IEEE DOI 2109
Training, Roads, Semantics, Employment, Training data, Benchmark testing, Particle measurements BibRef

Zhang, Y.[Youshan], Davison, B.D.[Brian D.],
Deep Spherical Manifold Gaussian Kernel for Unsupervised Domain Adaptation,
Diff-CVML21(4438-4447)
IEEE DOI 2109
Manifolds, Adaptation models, Feature extraction, Pattern recognition, Noise measurement BibRef

Jing, T.T.[Tao-Tao], Ding, Z.M.[Zheng-Ming],
Adversarial Dual Distinct Classifiers for Unsupervised Domain Adaptation,
WACV21(605-614)
IEEE DOI 2106
Visualization, Adaptation models, Target recognition, Benchmark testing, Feature extraction BibRef

Yeh, H.W.[Hao-Wei], Yang, B.[Baoyao], Yuen, P.C.[Pong C.], Harada, T.[Tatsuya],
SoFA: Source-data-free Feature Alignment for Unsupervised Domain Adaptation,
WACV21(474-483)
IEEE DOI 2106
Training, Adaptation models, Data privacy, Semantics, Predictive models, Gaussian distribution, Feature extraction BibRef

Ringwald, T.[Tobias], Stiefelhagen, R.[Rainer],
Adaptiope: A Modern Benchmark for Unsupervised Domain Adaptation,
WACV21(101-110)
IEEE DOI 2106
Systematics, Annotations, Training data, Benchmark testing, Cleaning BibRef

Azzam, M.[Mohamed], Gnanha, A.T.[Aurele Tohokantche], Wong, H.S.[Hau-San], Wu, S.[Si],
Adversarially Constrained Interpolation for Unsupervised Domain Adaptation,
ICPR21(2375-2381)
IEEE DOI 2105
Manifolds, Training, Interpolation, Adaptation models, Smoothing methods, Data models, Pattern recognition BibRef

Tran, H.H.[Hai H.], Ahn, S.[Sumyeong], Lee, T.[Taeyoung], Yi, Y.[Yung],
Enlarging Discriminative Power by Adding an Extra Class in Unsupervised Domain Adaptation,
ICPR21(1812-1819)
IEEE DOI 2105
Training, Adaptation models, Clustering algorithms, Predictive models, Feature extraction, Data models, Data mining BibRef

Liu, X.F.[Xiao-Feng], Hu, B.[Bo], Liu, X.[Xiongchang], Lu, J.[Jun], You, J.[Jane], Kong, L.[Lingsheng],
Energy-constrained Self-training for Unsupervised Domain Adaptation,
ICPR21(7515-7520)
IEEE DOI 2105
Training, Adaptation models, Image segmentation, Semantics, Supervised learning, Minimization, Pattern recognition BibRef

Xiao, R.X.[Rui-Xin], Liu, Z.L.[Zhi-Lei], Wu, B.Y.[Bao-Yuan],
Teacher-Student Competition for Unsupervised Domain Adaptation,
ICPR21(8291-8298)
IEEE DOI 2105
Training, Adaptation models, Benchmark testing, Feature extraction, Pattern recognition BibRef

Dai, S.Y.[Shu-Yang], Cheng, Y.[Yu], Zhang, Y.[Yizhe], Gan, Z.[Zhe], Liu, J.J.[Jing-Jing], Carin, L.[Lawrence],
Contrastively Smoothed Class Alignment for Unsupervised Domain Adaptation,
ACCV20(IV:268-283).
Springer DOI 2103
BibRef

Zhang, Y.S.[You-Shan], Ye, H.[Hui], Davison, B.D.[Brian D.],
Adversarial Reinforcement Learning for Unsupervised Domain Adaptation,
WACV21(635-644)
IEEE DOI 2106
BibRef
Earlier: A1, A3, Only:
Adversarial Continuous Learning in Unsupervised Domain Adaptation,
DLPR20(672-687).
Springer DOI 2103
Adaptation models, Computational modeling, Neural networks, Reinforcement learning, Feature extraction. BibRef

Cicek, S.[Safa], Xu, N.[Ning], Wang, Z.[Zhaowen], Jin, H.L.[Hai-Lin], Soatto, S.[Stefano],
Spatial Class Distribution Shift in Unsupervised Domain Adaptation: Local Alignment Comes to Rescue,
ACCV20(III:623-638).
Springer DOI 2103
BibRef

Li, C.C.[Cong-Cong], Du, D.[Dawei], Zhang, L.[Libo], Wen, L.Y.[Long-Yin], Luo, T.J.[Tie-Jian], Wu, Y.J.[Yan-Jun], Zhu, P.F.[Peng-Fei],
Spatial Attention Pyramid Network for Unsupervised Domain Adaptation,
ECCV20(XIII:481-497).
Springer DOI 2011
BibRef

Lee, J., Lee, G.,
Model Uncertainty for Unsupervised Domain Adaptation,
ICIP20(1841-1845)
IEEE DOI 2011
Uncertainty, Adaptation models, Feature extraction, Task analysis, Bayes methods, Mathematical model, Monte Carlo methods, image classification BibRef

Mei, K.[Ke], Zhu, C.[Chuang], Zou, J.Q.[Jia-Qi], Zhang, S.H.[Shang-Hang],
Instance Adaptive Self-training for Unsupervised Domain Adaptation,
ECCV20(XXVI:415-430).
Springer DOI 2011
BibRef

Peng, X.C.[Xing-Chao], Li, Y.C.[Yi-Chen], Saenko, K.[Kate],
Domain2vec: Domain Embedding for Unsupervised Domain Adaptation,
ECCV20(VI:756-774).
Springer DOI 2011
BibRef

Dong, J.H.[Jia-Hua], Cong, Y.[Yang], Sun, G.[Gan], Liu, Y.Y.[Yu-Yang], Xu, X.W.[Xiao-Wei],
CSCL: Critical Semantic-consistent Learning for Unsupervised Domain Adaptation,
ECCV20(VIII:745-762).
Springer DOI 2011
BibRef

Zhang, Y.B.[Ya-Bin], Deng, B.[Bin], Jia, K.[Kui], Zhang, L.[Lei],
Label Propagation with Augmented Anchors: A Simple Semi-supervised Learning Baseline for Unsupervised Domain Adaptation,
ECCV20(IV:781-797).
Springer DOI 2011
BibRef

Li, M., Zhai, Y., Luo, Y., Ge, P., Ren, C.,
Enhanced Transport Distance for Unsupervised Domain Adaptation,
CVPR20(13933-13941)
IEEE DOI 2008
Feature extraction, Training, Measurement, Adaptation models, Task analysis, Image reconstruction, Neural networks BibRef

Ye, S., Wu, K., Zhou, M., Yang, Y., Tan, S.H., Xu, K., Song, J., Bao, C., Ma, K.,
Light-weight Calibrator: A Separable Component for Unsupervised Domain Adaptation,
CVPR20(13733-13742)
IEEE DOI 2008
Adaptation models, Training, Feature extraction, Neural networks, Data models, Performance evaluation BibRef

Lu, Z., Yang, Y., Zhu, X., Liu, C., Song, Y., Xiang, T.,
Stochastic Classifiers for Unsupervised Domain Adaptation,
CVPR20(9108-9117)
IEEE DOI 2008
Training, Stochastic processes, Task analysis, Semantics, Neural networks, Data models, Adaptation models BibRef

Xu, R., Liu, P., Wang, L., Chen, C., Wang, J.,
Reliable Weighted Optimal Transport for Unsupervised Domain Adaptation,
CVPR20(4393-4402)
IEEE DOI 2008
Reliability, Kernel, Training, Generators, Task analysis, Measurement uncertainty BibRef

Hu, L., Kan, M., Shan, S., Chen, X.,
Unsupervised Domain Adaptation With Hierarchical Gradient Synchronization,
CVPR20(4042-4051)
IEEE DOI 2008
Feature extraction, Synchronization, Entropy, Task analysis, Training, Adaptation models BibRef

Cohen, T.[Tomer], Wolf, L.B.[Lior B.],
Bidirectional One-Shot Unsupervised Domain Mapping,
ICCV19(1784-1792)
IEEE DOI 2004
Code, Learning.
WWW Link. image processing, unsupervised learning, single sample domain, bidirectional one-shot unsupervised domain mapping, Unsupervised learning BibRef

Lee, S., Kim, D., Kim, N., Jeong, S.,
Drop to Adapt: Learning Discriminative Features for Unsupervised Domain Adaptation,
ICCV19(91-100)
IEEE DOI 2004
Code, Domain Adaption.
WWW Link. feature extraction, image classification, image representation, image segmentation, unsupervised learning, Neurons BibRef

Hou, J., Ding, X., Deng, J.D., Cranefield, S.,
Unsupervised Domain Adaptation using Deep Networks with Cross-Grafted Stacks,
TASKCV19(3257-3264)
IEEE DOI 2004
computer vision, feature extraction, image representation, learning (artificial intelligence), neural nets, Cross grafted Stacks BibRef

Gholami, B., Sahu, P., Kim, M., Pavlovic, V.,
Task-Discriminative Domain Alignment for Unsupervised Domain Adaptation,
MDALC19(1327-1336)
IEEE DOI 2004
data structures, pattern clustering, unsupervised learning, data structure, unsupervised domain adaptation, Stochastic embedding BibRef

Deng, Z., Luo, Y., Zhu, J.,
Cluster Alignment With a Teacher for Unsupervised Domain Adaptation,
ICCV19(9943-9952)
IEEE DOI 2004
pattern classification, pattern clustering, unsupervised learning, cluster alignment, labeled source domain, Labeling BibRef

Kim, S., Choi, J., Kim, T., Kim, C.,
Self-Training and Adversarial Background Regularization for Unsupervised Domain Adaptive One-Stage Object Detection,
ICCV19(6091-6100)
IEEE DOI 2004
feature extraction, object detection, unsupervised learning, BSR, target backgrounds, domain shift, foregrounds, Semantics BibRef

Xu, R., Li, G., Yang, J., Lin, L.,
Larger Norm More Transferable: An Adaptive Feature Norm Approach for Unsupervised Domain Adaptation,
ICCV19(1426-1435)
IEEE DOI 2004
Code, Domain Adaption.
WWW Link. learning (artificial intelligence), task-specific features, standard domain adaptation, partial domain adaptation, Neural networks BibRef

Binkowski, M., Hjelm, D., Courville, A.,
Batch Weight for Domain Adaptation With Mass Shift,
ICCV19(1844-1853)
IEEE DOI 2004
Bayes methods, language translation, probability, unsupervised learning, transfer networks, Task analysis BibRef

Kim, M.Y.[Min-Young], Sahu, P.[Pritish], Gholami, B.[Behnam], Pavlovic, V.[Vladimir],
Unsupervised Visual Domain Adaptation: A Deep Max-Margin Gaussian Process Approach,
CVPR19(4375-4385).
IEEE DOI 2002
BibRef

Chen, C.Q.[Chao-Qi], Xie, W.P.[Wei-Ping], Huang, W.B.[Wen-Bing], Rong, Y.[Yu], Ding, X.H.[Xing-Hao], Huang, Y.[Yue], Xu, T.Y.[Ting-Yang], Huang, J.Z.[Jun-Zhou],
Progressive Feature Alignment for Unsupervised Domain Adaptation,
CVPR19(627-636).
IEEE DOI 2002
BibRef

Pan, Y.W.[Ying-Wei], Yao, T.[Ting], Li, Y.[Yehao], Wang, Y.[Yu], Ngo, C.W.[Chong-Wah], Mei, T.[Tao],
Transferrable Prototypical Networks for Unsupervised Domain Adaptation,
CVPR19(2234-2242).
IEEE DOI 2002
BibRef

Lee, C.Y.[Chen-Yu], Batra, T.[Tanmay], Baig, M.H.[Mohammad Haris], Ulbricht, D.[Daniel],
Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation,
CVPR19(10277-10287).
IEEE DOI 2002
BibRef

Chang, W.G.[Woong-Gi], You, T.[Tackgeun], Seo, S.[Seonguk], Kwak, S.[Suha], Han, B.H.[Bo-Hyung],
Domain-Specific Batch Normalization for Unsupervised Domain Adaptation,
CVPR19(7346-7354).
IEEE DOI 2002
BibRef

Kang, G.L.[Guo-Liang], Jiang, L.[Lu], Yang, Y.[Yi], Hauptmann, A.G.[Alexander G.],
Contrastive Adaptation Network for Unsupervised Domain Adaptation,
CVPR19(4888-4897).
IEEE DOI 2002
BibRef

Roy, S.[Subhankar], Siarohin, A.[Aliaksandr], Sangineto, E.[Enver], Bulo, S.R.[Samuel Rota], Sebe, N.[Nicu], Ricci, E.[Elisa],
Unsupervised Domain Adaptation Using Feature-Whitening and Consensus Loss,
CVPR19(9463-9472).
IEEE DOI 2002
BibRef

Roy, S.[Subhankar], Siarohin, A.[Aliaksandr], Sebe, N.[Nicu],
Unsupervised Domain Adaptation Using Full-Feature Whitening and Colouring,
CIAP19(II:225-236).
Springer DOI 1909
BibRef

Jamal, A.[Arshad], Namboodiri, V.P.[Vinay P.], Deodhare, D.[Dipti], Venkatesh, K.S.,
U-DADA: Unsupervised Deep Action Domain Adaptation,
ACCV18(III:444-459).
Springer DOI 1906
BibRef

Park, H.[Hyoungwoo], Ju, M.J.[Min-Jeong], Moon, S.K.[Sang-Keun], Yoo, C.D.[Chang D.],
Unsupervised Domain Adaptation for Object Detection Using Distribution Matching in Various Feature Level,
IWDW18(363-372).
Springer DOI 1905
BibRef

Saito, K., Watanabe, K., Ushiku, Y., Harada, T.,
Maximum Classifier Discrepancy for Unsupervised Domain Adaptation,
CVPR18(3723-3732)
IEEE DOI 1812
Generators, Task analysis, Training, Neural networks, Semantics, Feature extraction, Learning systems BibRef

Pinheiro, P.O.,
Unsupervised Domain Adaptation with Similarity Learning,
CVPR18(8004-8013)
IEEE DOI 1812
Prototypes, Adaptation models, Training, Pollution measurement, Standards BibRef

Yang, Z., Chen, W., Wang, F., Xu, B.,
Unsupervised Domain Adaptation for Neural Machine Translation,
ICPR18(338-343)
IEEE DOI 1812
Training, Adaptation models, Generators, Data models, Task analysis, Transforms, Feature extraction BibRef

Gui, C., Hu, J.,
Unsupervised Domain Adaptation by regularizing Softmax Activation,
ICPR18(397-402)
IEEE DOI 1812
Standards, Training, Entropy, Bridges, Feature extraction, Benchmark testing, Kernel BibRef

Xiao, P., Du, B., Yun, S., Lit, X., Zhang, Y., Wu, J.,
Probabilistic Graph Embedding for Unsupervised Domain Adaptation,
ICPR18(1283-1288)
IEEE DOI 1812
graph theory, matrix algebra, pattern classification, probability, unsupervised learning, unlabeled target domain data, Computational modeling BibRef

Das, D., Lee, C.S.G.[C. S. George],
Unsupervised Domain Adaptation Using Regularized Hyper-Graph Matching,
ICIP18(3758-3762)
IEEE DOI 1809
Principal component analysis, Optimization, Object recognition, Cats, Dogs, Tensile stress, Indexes, Domain Adaptation, Object Recognition BibRef

Zhu, L., Zhang, X., Zhang, W., Huang, X., Guan, N., Luo, Z.,
Unsupervised domain adaptation with joint supervised sparse coding and discriminative regularization term,
ICIP17(3066-3070)
IEEE DOI 1803
Encoding, Image reconstruction, Kernel, Learning systems, Optimization, Sparse matrices, Task analysis, Transfer learning, subspace learning BibRef

Csurka, G., Baradel, F., Chidlovskii, B., Clinchant, S.,
Discrepancy-Based Networks for Unsupervised Domain Adaptation: A Comparative Study,
TASKCV17(2630-2636)
IEEE DOI 1802
Adaptation models, Data models, Feature extraction, Kernel, Painting, Training BibRef

Gholami, B., Rudovic, O., Pavlovic, V.,
PUnDA: Probabilistic Unsupervised Domain Adaptation for Knowledge Transfer Across Visual Categories,
ICCV17(3601-3610)
IEEE DOI 1802
Bayes methods, image classification, unsupervised learning, PUnDA, classifier discriminative power, domain disparity, Visualization BibRef

Aljundi, R.[Rahaf], Tuytelaars, T.[Tinne],
Lightweight Unsupervised Domain Adaptation by Convolutional Filter Reconstruction,
TASKCV16(III: 508-515).
Springer DOI 1611
BibRef

Csurka, G.[Gabriela], Chidlowskii, B.[Boris], Clinchant, S.[Stéphane], Michel, S.[Sophia],
Unsupervised Domain Adaptation with Regularized Domain Instance Denoising,
TASKCV16(III: 458-466).
Springer DOI 1611
BibRef

Khan, M.N.A., Heisterkamp, D.R.,
Adapting instance weights for unsupervised domain adaptation using quadratic mutual information and subspace learning,
ICPR16(1560-1565)
IEEE DOI 1705
BibRef
And:
Domain adaptation by iterative improvement of soft-labeling and maximization of non-parametric mutual information,
ICIP16(4458-4462)
IEEE DOI 1610
Adaptation models, Data models, Iterative methods, Kernel, Labeling, Mutual information, Training. BibRef

Xu, M.W.[Ming-Wei], Wu, S.S.[Song-Song], Jing, X.Y.[Xiao-Yuan], Yang, J.Y.[Jing-Yu],
Kernel subspace alignment for unsupervised domain adaptation,
ICIP15(2880-2884)
IEEE DOI 1512
domain adaptation; kernel subspace alignment; object recognition BibRef

Caseiro, R.[Rui], Henriques, J.F.[Joao F.], Martins, P.[Pedro], Batista, J.P.[Jorge P.],
Beyond the shortest path: Unsupervised domain adaptation by Sampling Subspaces along the Spline Flow,
CVPR15(3846-3854)
IEEE DOI 1510
BibRef

Long, M.S.[Ming-Sheng], Wang, J.M.[Jian-Min], Ding, G.G.[Gui-Guang], Sun, J.G.[Jia-Guang], Yu, P.S.[Philip S.],
Transfer Joint Matching for Unsupervised Domain Adaptation,
CVPR14(1410-1417)
IEEE DOI 1409
BibRef
Earlier:
Transfer Feature Learning with Joint Distribution Adaptation,
ICCV13(2200-2207)
IEEE DOI 1403
distribution matching. Transfer learning; feature learning; joint distribution adaptation BibRef

Mirrashed, F.[Fatemeh], Morariu, V.I.[Vlad I.], Davis, L.S.[Larry S.],
Sampling for unsupervised domain adaptive object detection,
ICIP13(3288-3292)
IEEE DOI 1402
Domain Adaptation;Object Detection;Semi-supervised Learning BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Multi-Task Learning, Multiple Tasks, Transfer Learning, Domain Adaption .


Last update:Oct 24, 2021 at 16:35:58