14.1.4.4 Domain Adaptation

Dinh, C.V.[Cuong V.], Duin, R.P.W.[Robert P.W.], Piqueras-Salazar, I.[Ignacio], Loog, M.[Marco],
FIDOS: A generalized Fisher based feature extraction method for domain shift,
PR(46), No. 9, September 2013, pp. 2510-2518.
Elsevier DOI 1305
Fisher feature extraction; Invariant features; Domain shift; Domain adaptation; Multiple source domain adaptation BibRef

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

Moreno-Torres, J.G.[Jose G.], Raeder, T.[Troy], Alaiz-Rodríguez, R.[Rocío], Chawla, N.V.[Nitesh V.], Herrera, F.[Francisco],
A unifying view on dataset shift in classification,
PR(45), No. 1, 2012, pp. 521-530.
Elsevier DOI 1410
Dataset shift BibRef

Liu, Y.L.[Yi-Lun], Li, X.[Xia],
Domain adaptation for land use classification: A spatio-temporal knowledge reusing method,
PandRS(98), No. 1, 2014, pp. 133-144.
Elsevier DOI 1411
Domain adaptation BibRef

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

Morvant, E.[Emilie],
Domain adaptation of weighted majority votes via perturbed variation-based self-labeling,
PRL(51), No. 1, 2015, pp. 37-43.
Elsevier DOI 1412
Machine learning BibRef

Banerjee, B.[Biplab], Bovolo, F.[Francesca], Bhattacharya, A.[Avik], Bruzzone, L.[Lorenzo], Chaudhuri, S.[Subhasis], Buddhiraju, K.M.[Krishna Mohan],
A Novel Graph-Matching-Based Approach for Domain Adaptation in Classification of Remote Sensing Image Pair,
GeoRS(53), No. 7, July 2015, pp. 4045-4062.
IEEE DOI 1503
Clustering algorithms BibRef

Qin, Y.[Yao], Bruzzone, L.[Lorenzo], Li, B.[Biao],
Tensor Alignment Based Domain Adaptation for Hyperspectral Image Classification,
GeoRS(57), No. 11, November 2019, pp. 9290-9307.
IEEE DOI 1911
Hyperspectral imaging, Manifolds, Image reconstruction, Matrix decomposition, Task analysis, Domain adaptation (DA), tensor alignment (TA) BibRef

Banerjee, B.[Biplab], Mishra, P.K.[Pradeep Kumar], Varma, S.[Surender], Mohan, B.K.[Buddhiraju Krishna],
A Novel Graph Based Clustering Technique for Hybrid Segmentation of Multi-spectral Remotely Sensed Images,
ACIVS13(274-285).
Springer DOI 1311
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

Zhang, Y.H.[Yu-Hong], Hu, X.G.[Xue-Gang], Li, P.P.[Pei-Pei], Li, L.[Lei], Wu, X.D.[Xin-Dong],
Cross-domain sentiment classification-feature divergence, polarity divergence or both?,
PRL(65), No. 1, 2015, pp. 44-50.
Elsevier DOI 1511
Sentiment classification BibRef

Wang, Y.[Yu], Li, J.H.[Ji-Hong], Li, Y.F.[Yan-Fang],
Measure for data partitioning in mX2 cross-validation,
PRL(65), No. 1, 2015, pp. 211-217.
Elsevier DOI 1511
Data partitioning BibRef

Mozafari, A.S.[Azadeh Sadat], Jamzad, M.[Mansour],
A SVM-based model-transferring method for heterogeneous domain adaptation,
PR(56), No. 1, 2016, pp. 142-158.
Elsevier DOI 1604
BibRef
Earlier:
Heterogeneous domain adaptation using previously learned classifier for object detection problem,
ICIP14(4077-4081)
IEEE DOI 1502
SVM-based method. Accuracy BibRef

Mozafari, A.S.[Azadeh Sadat], Jamzad, M.[Mansour],
Cluster-based adaptive SVM: A latent subdomains discovery method for domain adaptation problems,
CVIU(162), No. 1, 2017, pp. 116-134.
Elsevier DOI 1710
SVM-based, da, method 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., Crawford, M.M., Zhu, L., Liu, Y.,
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

Hou, C.A.[Cheng-An], Tsai, Y.H.H.[Yao-Hung Hubert], Yeh, Y.R.[Yi-Ren], Wang, Y.C.F.[Yu-Chiang 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.F.[Yu-Chiang 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.F.[Yu-Chiang 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.F.[Yu-Chiang 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

Jiang, M., Huang, W., Huang, Z., Yen, G.G.,
Integration of Global and Local Metrics for Domain Adaptation Learning Via Dimensionality Reduction,
Cyber(47), No. 1, January 2017, pp. 38-51.
IEEE DOI 1612
Algorithm design and analysis 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

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

Lu, H.[Hao], Cao, Z.G.[Zhi-Guo], Xiao, Y.[Yang], Zhu, Y.J.[Yan-Jun],
Two-dimensional subspace alignment for convolutional activations adaptation,
PR(71), No. 1, 2017, pp. 320-336.
Elsevier DOI 1707
Visual domain adaptation BibRef

Othman, E., Bazi, Y., Melgani, F., Alhichri, H., Alajlan, N., Zuair, M.,
Domain Adaptation Network for Cross-Scene Classification,
GeoRS(55), No. 8, August 2017, pp. 4441-4456.
IEEE DOI 1708
Computer architecture, Earth, Feature extraction, Feeds, Machine learning, Neural networks, Remote sensing, Cross-scene classification, distribution mismatch, domain adaptation, multisensor data, pretrained, convolutional, neural, network, (CNN) BibRef

Li, J., Wu, Y., Lu, K.,
Structured Domain Adaptation,
CirSysVideo(27), No. 8, August 2017, pp. 1700-1713.
IEEE DOI 1708
Adaptation models, Bridge circuits, Feature extraction, Image reconstruction, Robustness, Videos, Visualization, Domain adaptation, structured reconstruction, subspace learning, transfer, learning 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

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

Paris, C., Bruzzone, L.,
A Sensor-Driven Hierarchical Method for Domain Adaptation in Classification of Remote Sensing Images,
GeoRS(56), No. 3, March 2018, pp. 1308-1324.
IEEE DOI 1804
feature extraction, image classification, learning (artificial intelligence), pattern classification, transfer learning BibRef

Li, W.[Wen], Xu, Z.[Zheng], Xu, D.[Dong], Dai, D.X.[Deng-Xin], Van Gool, L.J.[Luc J.],
Domain Generalization and Adaptation Using Low Rank Exemplar SVMs,
PAMI(40), No. 5, May 2018, pp. 1114-1127.
IEEE DOI 1804
Linear programming, Logistics, Support vector machines, Testing, Training, Videos, Visualization, Latent domains, domain adaptation, exemplar SVMs BibRef

Wang, Y., Li, W., Dai, D.X.[Deng-Xin], Van Gool, L.J.[Luc J.],
Deep Domain Adaptation by Geodesic Distance Minimization,
TASKCV17(2651-2657)
IEEE DOI 1802
Adaptation models, Covariance matrices, Euclidean distance, Feature extraction, Manifolds, Training data, Visualization BibRef

Fang, W.C.[Wen-Chieh], Chiang, Y.T.[Yi-Ting],
A discriminative feature mapping approach to heterogeneous domain adaptation,
PRL(106), 2018, pp. 13-19.
Elsevier DOI 1804
Heterogeneous domain adaptation, Data projections, Feature learning, Supervised classification, Machine learning 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

Li, Y.H.[Yang-Hao], Wang, N.Y.[Nai-Yan], Shi, J.P.[Jian-Ping], Hou, X.D.[Xiao-Di], Liu, J.Y.[Jia-Ying],
Adaptive Batch Normalization for practical domain adaptation,
PR(80), 2018, pp. 109-117.
Elsevier DOI 1805
Domain adaptation, Batch normalization, Neural networks BibRef

Yan, L.[Li], Zhu, R.[Ruixi], Liu, Y.[Yi], Mo, N.[Nan],
Color-Boosted Saliency-Guided Rotation Invariant Bag of Visual Words Representation with Parameter Transfer for Cross-Domain Scene-Level Classification,
RS(10), No. 4, 2018, pp. xx-yy.
DOI Link 1805
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

Li, S., Song, S., Huang, G., Ding, Z., Wu, C.,
Domain Invariant and Class Discriminative Feature Learning for Visual Domain Adaptation,
IP(27), No. 9, September 2018, pp. 4260-4273.
IEEE DOI 1807
feature extraction, image classification, image representation, learning (artificial intelligence), optimisation, DICD, subspace learning BibRef

Aytar, Y.[Yusuf], Castrejón, L.[Lluís], Vondrick, C.[Carl], Pirsiavash, H.[Hamed], Torralba, A.B.[Antonio B.],
Cross-Modal Scene Networks,
PAMI(40), No. 10, October 2018, pp. 2303-2314.
IEEE DOI 1809
BibRef
Earlier: A2, A1, A3, A4, A5:
Learning Aligned Cross-Modal Representations from Weakly Aligned Data,
CVPR16(2940-2949)
IEEE DOI 1612
Training, Visualization, Art, Automobiles, Data models, Adaptation models, Measurement, Cross-modal perception, scene understanding 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

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

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

Zhou, X., Prasad, S.,
Deep Feature Alignment Neural Networks for Domain Adaptation of Hyperspectral Data,
GeoRS(56), No. 10, October 2018, pp. 5863-5872.
IEEE DOI 1810
Feature extraction, Hyperspectral imaging, Neural networks, Adaptation models, Training data, Machine learning, Classification, transformation learning BibRef

Rozantsev, A., Salzmann, M., Fua, P.,
Beyond Sharing Weights for Deep Domain Adaptation,
PAMI(41), No. 4, April 2019, pp. 801-814.
IEEE DOI 1903
BibRef
Earlier:
Residual Parameter Transfer for Deep Domain Adaptation,
CVPR18(4339-4348)
IEEE DOI 1812
Training, Machine learning, Task analysis, Computer architecture, Computer vision, Training data, Detectors, Domain adaptation, deep learning. Transforms, Streaming media, Complexity theory, Feature extraction, Adaptation models. 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

Gross, W., Tuia, D., Soergel, U., Middelmann, W.,
Nonlinear Feature Normalization for Hyperspectral Domain Adaptation and Mitigation of Nonlinear Effects,
GeoRS(57), No. 8, August 2019, pp. 5975-5990.
IEEE DOI 1908
hyperspectral imaging, image classification, image representation, image sampling, remote sensing, mitigating nonlinearities 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

Mehrkanoon, S.[Siamak],
Cross-domain neural-kernel networks,
PRL(125), 2019, pp. 474-480.
Elsevier DOI 1909
Domain adaptation, Neural networks, Kernel methods, Coupling regularization 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

Li, J., Jing, M., Lu, K., Zhu, L., Shen, H.T.,
Locality Preserving Joint Transfer for Domain Adaptation,
IP(28), No. 12, December 2019, pp. 6103-6115.
IEEE DOI 1909
Knowledge transfer, Adaptation models, Manifolds, Optimization, Task analysis, Feature extraction, Dimensionality reduction, subspace learning BibRef

Li, H., Wang, X., Shen, F., Li, Y., Porikli, F., Wang, M.,
Real-Time Deep Tracking via Corrective Domain Adaptation,
CirSysVideo(29), No. 9, September 2019, pp. 2600-2612.
IEEE DOI 1909
Target tracking, Visualization, Feature extraction, Real-time systems, Detectors, Task analysis, Deep learning, real-time 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

Chen, Y., Song, S., Li, S., Wu, C.,
A Graph Embedding Framework for Maximum Mean Discrepancy-Based Domain Adaptation Algorithms,
IP(29), No. 1, 2020, pp. 199-213.
IEEE DOI 1910
data handling, feature extraction, graph theory, learning (artificial intelligence), optimisation, graph embedding BibRef

Garea, A.S.[Alberto S.], Heras, D.B.[Dora B.], Argüello, F.[Francisco],
TCANet for Domain Adaptation of Hyperspectral Images,
RS(11), No. 19, 2019, pp. xx-yy.
DOI Link 1910
BibRef

Liu, Y., Tu, W., Du, B., Zhang, L., Tao, D.,
Homologous Component Analysis for Domain Adaptation,
IP(29), No. , 2020, pp. 1074-1089.
IEEE DOI 1911
Kernel, Computer science, Adaptation models, Visualization, Training, Hilbert space, Visual domain adaptation, visual categorization BibRef

Ma, X.R.[Xiao-Rui], Mou, X.R.[Xue-Rong], Wang, J.[Jie], Liu, X.K.[Xiao-Kai], Wang, H.Y.[Hong-Yu], Yin, B.C.[Bao-Cai],
Cross-Data Set Hyperspectral Image Classification Based on Deep Domain Adaptation,
GeoRS(57), No. 12, December 2019, pp. 10164-10174.
IEEE DOI 1912
Task analysis, Hyperspectral imaging, Training, Deep learning, Feature extraction, Cross-data set classification, neural networks BibRef

Yang, F., Chang, J., Tsai, C., Wang, Y.F.,
A Multi-Domain and Multi-Modal Representation Disentangler for Cross-Domain Image Manipulation and Classification,
IP(29), 2020, pp. 2795-2807.
IEEE DOI 2001
Representation disentanglement, image translation, domain adaptation, deep 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

Yao, Y.[Yuan], Zhang, Y.[Yu], Li, X.[Xutao], Ye, Y.M.[Yun-Ming],
Discriminative distribution alignment: A unified framework for heterogeneous domain adaptation,
PR(101), 2020, pp. 107165.
Elsevier DOI 2003
Heterogeneous domain adaptation, Subspace learning, Classifier adaptation, Distribution alignment, Discriminative embedding BibRef

Sun, W.D.[Wei-Dong], Li, P.X.[Ping-Xiang], Du, B.[Bo], Yang, J.[Jie], Tian, L.L.[Lin-Lin], Li, M.[Minyi], Zhao, L.[Lingli],
Scatter Matrix Based Domain Adaptation for Bi-Temporal Polarimetric SAR Images,
RS(12), No. 4, 2020, pp. xx-yy.
DOI Link 2003
BibRef

Li, W.[Wei], Wang, M.[Meng], Wang, H.B.[Hong-Bin], Zhang, Y.[Yafei],
Object detection based on semi-supervised domain adaptation for imbalanced domain resources,
MVA(31), No. 3, March 2020, pp. Article18.
WWW Link. 2004
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

Han, C.[Chao], Lei, Y.[Yu], Xie, Y.[Yu], Zhou, D.[Deyun], Gong, M.[Maoguo],
Visual domain adaptation based on modified A-distance and sparse filtering,
PR(104), 2020, pp. 107254.
Elsevier DOI 2005
Domain adaptation, distance, Sparse filtering BibRef

Zakharov, S.[Sergey], Kehl, W.[Wadim], Ilic, S.[Slobodan],
DeceptionNet: Network-Driven Domain Randomization,
ICCV19(532-541)
IEEE DOI 2004
Move from synthetic data to real. image colour analysis, image enhancement, image sampling, image segmentation, minimax techniques, neural nets, Robustness 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

Xie, R., Yu, F., Wang, J., Wang, Y., Zhang, L.,
Multi-Level Domain Adaptive Learning for Cross-Domain Detection,
TASKCV19(3213-3219)
IEEE DOI 2004
convolutional neural nets, feature extraction, image classification, image sensors, Adversarial Learning 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

Kuhnke, F., Ostermann, J.,
Deep Head Pose Estimation Using Synthetic Images and Partial Adversarial Domain Adaption for Continuous Label Spaces,
ICCV19(10163-10172)
IEEE DOI 2004
learning (artificial intelligence), object recognition, pose estimation, rendering (computer graphics), solid modelling, Face 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

Feng, Q., Kang, G., Fan, H., Yang, Y.,
Attract or Distract: Exploit the Margin of Open Set,
ICCV19(7989-7998)
IEEE DOI 2004
data handling, decision theory, pattern classification, set theory, domain adaptation, domain shift, semantic structure, open set data, Benchmark testing BibRef

Saito, K., Kim, D., Sclaroff, S., Darrell, T., 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

Spezialetti, R., Salti, S., Stefano, L.D.,
Learning an Effective Equivariant 3D Descriptor Without Supervision,
ICCV19(6400-6409)
IEEE DOI 2004
convolutional neural nets, feature extraction, image classification, image matching, image representation, Proposals BibRef

Ramirez, P.Z., Tonioni, A., Salti, S., Stefano, L.D.,
Learning Across Tasks and Domains,
ICCV19(8109-8118)
IEEE DOI 2004
computer vision, image segmentation, supervised learning, visual tasks, adaptation framework, fully supervised domain, Transforms BibRef

Balaji, Y., Chellappa, R., Feizi, S.,
Normalized Wasserstein for Mixture Distributions With Applications in Adversarial Learning and Domain Adaptation,
ICCV19(6499-6507)
IEEE DOI 2004
learning (artificial intelligence), neural nets, pattern clustering, statistical distributions, Adaptation models BibRef

Chen, M., Kira, Z., Alregib, G., Yoo, J., Chen, R., Zheng, J.,
Temporal Attentive Alignment for Large-Scale Video Domain Adaptation,
ICCV19(6320-6329)
IEEE DOI 2004
Code, Domain Adaption.
WWW Link. convolutional neural nets, image classification, learning (artificial intelligence), neural net architecture, Dynamics 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

Yue, X., Zhang, Y., Zhao, S., Sangiovanni-Vincentelli, A., Keutzer, K., Gong, B.,
Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization Without Accessing Target Domain Data,
ICCV19(2100-2110)
IEEE DOI 2004
computer vision, feature extraction, image representation, image segmentation, learning (artificial intelligence), Adaptation models BibRef

Wang, J., Jiang, J.,
Conditional Coupled Generative Adversarial Networks for Zero-Shot Domain Adaptation,
ICCV19(3374-3383)
IEEE DOI 2004
computer vision, image classification, learning (artificial intelligence), neural nets, CoCoGAN, BibRef

Chen, M., Xue, H., Cai, D.,
Domain Adaptation for Semantic Segmentation With Maximum Squares Loss,
ICCV19(2090-2099)
IEEE DOI 2004
Code, Domain Adaption.
WWW Link. entropy, image segmentation, minimisation, neural nets, supervised learning, semantic segmentation, maximum squares loss, Training 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

Li, D., Zhang, J., Yang, Y., Liu, C., Song, Y., Hospedales, T.,
Episodic Training for Domain Generalization,
ICCV19(1446-1455)
IEEE DOI 2004
computer vision, convolutional neural nets, feature extraction, generalisation (artificial intelligence), Data models BibRef

Tsai, Y., Sohn, K., Schulter, S., Chandraker, M.,
Domain Adaptation for Structured Output via Discriminative Patch Representations,
ICCV19(1456-1465)
IEEE DOI 2004
convolutional neural nets, image representation, learning (artificial intelligence), Indexes 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, T.[Taekyung], Jeong, M.[Minki], Kim, S.[Seunghyeon], Choi, S.[Seokeon], Kim, C.[Changick],
Diversify and Match: A Domain Adaptive Representation Learning Paradigm for Object Detection,
CVPR19(12448-12457).
IEEE DOI 2002
BibRef

Cao, Z.J.[Zhang-Jie], You, K.[Kaichao], Long, M.S.[Ming-Sheng], Wang, J.M.[Jian-Min], Yang, Q.A.[Qi-Ang],
Learning to Transfer Examples for Partial Domain Adaptation,
CVPR19(2980-2989).
IEEE DOI 2002
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

Liu, H.[Hong], Cao, Z.[Zhangjie], Long, M.S.[Ming-Sheng], Wang, J.[Jianmin], Yang, Q.A.[Qi-Ang],
Separate to Adapt: Open Set Domain Adaptation via Progressive Separation,
CVPR19(2922-2931).
IEEE DOI 2002
BibRef

Liang, J.[Jian], He, R.[Ran], Sun, Z.A.[Zhen-An], Tan, T.N.[Tie-Niu],
Distant Supervised Centroid Shift: A Simple and Efficient Approach to Visual Domain Adaptation,
CVPR19(2970-2979).
IEEE DOI 2002
BibRef

Xu, X.[Xiang], Zhou, X.[Xiong], Venkatesan, R.[Ragav], Swaminathan, G.[Gurumurthy], Majumder, O.[Orchid],
d-SNE: Domain Adaptation Using Stochastic Neighborhood Embedding,
CVPR19(2492-2501).
IEEE DOI 2002
BibRef

Tran, L.[Luan], Sohn, K.[Kihyuk], Yu, X.[Xiang], Liu, X.M.[Xiao-Ming], Chandraker, M.[Manmohan],
Gotta Adapt 'Em All: Joint Pixel and Feature-Level Domain Adaptation for Recognition in the Wild,
CVPR19(2667-2676).
IEEE DOI 2002
BibRef

You, K.[Kaichao], Long, M.S.[Ming-Sheng], Cao, Z.[Zhangjie], Wang, J.[Jianmin], Jordan, M.I.[Michael I.],
Universal Domain Adaptation,
CVPR19(2715-2724).
IEEE DOI 2002
BibRef

Kurmi, V.K.[Vinod Kumar], Kumar, S.[Shanu], Namboodiri, V.P.[Vinay P.],
Attending to Discriminative Certainty for Domain Adaptation,
CVPR19(491-500).
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

Mancini, M.[Massimiliano], Bulo, S.R.[Samuel Rota], Caputo, B.[Barbara], Ricci, E.[Elisa],
AdaGraph: Unifying Predictive and Continuous Domain Adaptation Through Graphs,
CVPR19(6561-6570).
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

Bapat, A.[Akash], Frahm, J.M.[Jan-Michael],
The Domain Transform Solver,
CVPR19(6007-6016).
IEEE DOI 2002
BibRef

Tan, S.[Shuhan], Jiao, J.[Jiening], Zheng, W.S.[Wei-Shi],
Weakly Supervised Open-Set Domain Adaptation by Dual-Domain Collaboration,
CVPR19(5389-5398).
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

Osumi, K., Yamashita, T., Fujiyoshi, H.,
Domain Adaptation using a Gradient Reversal Layer with Instance Weighting,
MVA19(1-5)
DOI Link 1911
data handling, learning (artificial intelligence), gradient reversal layer, instance weighting, GRL, target domain, Transmitters BibRef

Fu, J., Wu, X., Zhang, S., Yan, J.,
Improved Open Set Domain Adaptation with Backpropagation,
ICIP19(2506-2510)
IEEE DOI 1910
Open set domain adaptation, Back propagation, Symmetrical Kullback Leibler distance BibRef

Nag, S., Adak, S., Das, S.,
What's There in the Dark,
ICIP19(2996-3000)
IEEE DOI 1910
Night scene Segmentation, Deep Learning, Domain Adaptation (DA), Multi-scale Patch Fusion. BibRef

Bascol, K., Emonet, R., Fromont, É.,
Improving Domain Adaptation by Source Selection,
ICIP19(3043-3047)
IEEE DOI 1910
Domain Adaptation, Negative Transfer, Deep Learning, Image Classification BibRef

Bucci, S.[Silvia], d'Innocente, A.[Antonio], Tommasi, T.[Tatiana],
Tackling Partial Domain Adaptation with Self-supervision,
CIAP19(II:70-81).
Springer DOI 1909
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

d'Innocente, A.[Antonio], Caputo, B.[Barbara],
Domain Generalization with Domain-Specific Aggregation Modules,
GCPR18(187-198).
Springer DOI 1905
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

Liu, Y.C.[Yen-Cheng], Yeh, Y.Y.[Yu-Ying], Fu, T.C.[Tzu-Chien], Wang, S.D.[Sheng-De], Chiu, W.C.[Wei-Chen], Wang, Y.C.F.[Yu-Chiang Frank],
Detach and Adapt: Learning Cross-Domain Disentangled Deep Representation,
CVPR18(8867-8876)
IEEE DOI 1812
Task analysis, Adaptation models, Training, Generators, Neural networks, Visualization BibRef

Peng, X., Usman, B., Kaushik, N., Wang, D., Hoffman, J., Saenko, K.,
VisDA: A Synthetic-to-Real Benchmark for Visual Domain Adaptation,
DeepLearnRV18(2102-21025)
IEEE DOI 1812
Training, Adaptation models, Image segmentation, Benchmark testing, Task analysis, Semantics BibRef

Zhang, Z., Wang, M., Huang, Y., Nehorai, A.,
Aligning Infinite-Dimensional Covariance Matrices in Reproducing Kernel Hilbert Spaces for Domain Adaptation,
CVPR18(3437-3445)
IEEE DOI 1812
Kernel, Covariance matrices, Correlation, Maximum likelihood estimation, Hilbert space, Testing, Computational modeling 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

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

Mancini, M.[Massimiliano], Porzi, L.[Lorenzo], Bulò, S.R., Caputo, B.[Barbara], Ricci, E.,
Boosting Domain Adaptation by Discovering Latent Domains,
CVPR18(3771-3780)
IEEE DOI 1812
Data models, Adaptation models, Neural networks, Computer vision, Computer architecture, Training data, Training BibRef

Xu, R., Chen, Z., Zuo, W., Yan, J., Lin, L.,
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

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

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

Koniusz, P.[Piotr], Tas, Y.[Yusuf], Zhang, H.[Hongguang], Harandi, M.[Mehrtash], Porikli, F.M.[Fatih M.], Zhang, R.[Rui],
Museum Exhibit Identification Challenge for the Supervised Domain Adaptation and Beyond,
ECCV18(XVI: 815-833).
Springer DOI 1810
BibRef

Peng, K.C.[Kuan-Chuan], Wu, Z.[Ziyan], Ernst, J.[Jan],
Zero-Shot Deep Domain Adaptation,
ECCV18(XI: 793-810).
Springer DOI 1810
BibRef

Saito, K.[Kuniaki], Yamamoto, S.[Shohei], Ushiku, Y.[Yoshitaka], Harada, T.[Tatsuya],
Open Set Domain Adaptation by Backpropagation,
ECCV18(VI: 156-171).
Springer DOI 1810
BibRef

Zhang, Y.[Yue], Miao, S.[Shun], Liao, R.[Rui],
Structural Domain Adaptation with Latent Graph Alignment,
ICIP18(3753-3757)
IEEE DOI 1809
Adaptation models, Laplace equations, Manifolds, Eigenvalues and eigenfunctions, Optimization, Measurement, Alternating Minimization 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

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, image recognition 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

Haeusser, P., Frerix, T., Mordvintsev, A., Cremers, D.[Daniel],
Associative Domain Adaptation,
ICCV17(2784-2792)
IEEE DOI 1802
convolution, neural net architecture, pattern classification, statistical analysis, association loss, Training BibRef

Masana, M., van de Weijer, J.[Joost], Herranz, L., Bagdanov, A.D., Álvarez, J.M.,
Domain-Adaptive Deep Network Compression,
ICCV17(4299-4307)
IEEE DOI 1802
image coding, image representation, learning (artificial intelligence), matrix decomposition, Training BibRef

Motiian, S., Piccirilli, M., Adjeroh, D.A., Doretto, G.,
Unified Deep Supervised Domain Adaptation and Generalization,
ICCV17(5716-5726)
IEEE DOI 1802
feature extraction, image representation, learning (artificial intelligence), statistical distributions, Visualization 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

Sarafianos, N., Vrigkas, M., Kakadiaris, I.A.,
Adaptive SVM+: Learning with Privileged Information for Domain Adaptation,
TASKCV17(2637-2644)
IEEE DOI 1802
Feature extraction, Linear programming, Support vector machines, Testing, Training, Visualization BibRef

Patricia, N., Cariucci, F.M., Caputo, B.,
Deep Depth Domain Adaptation: A Case Study,
TASKCV17(2645-2650)
IEEE DOI 1802
Adaptation models, Benchmark testing, Databases, Gray-scale, Image color analysis, Robots, 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

Wu, C., Wen, W., Afzal, T., Zhang, Y., Chen, Y., Li, H.,
A Compact DNN: Approaching GoogLeNet-Level Accuracy of Classification and Domain Adaptation,
CVPR17(761-770)
IEEE DOI 1711
Adaptation models, Convolution, Deconvolution, Feature extraction, Image coding, Kernel BibRef

Yan, H., Ding, Y., Li, P., Wang, Q., Xu, Y., Zuo, W.,
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

Koniusz, P., Tas, Y., Porikli, F.M.[Fatih M.],
Domain Adaptation by Mixture of Alignments of Second-or Higher-Order Scatter Tensors,
CVPR17(7139-7148)
IEEE DOI 1711
Correlation, Streaming media, Tensile stress, Training, Visualization BibRef

Herath, S., Harandi, M.T.[Mehrtash T.], Porikli, F.M.[Fatih M.],
Learning an Invariant Hilbert Space for Domain Adaptation,
CVPR17(3956-3965)
IEEE DOI 1711
Color, Covariance matrices, Hilbert space, Manifolds, Optimization, Proposals BibRef

Chen, S., Zhou, F., Liao, Q.,
Visual domain adaptation using weighted subspace alignment,
VCIP16(1-4)
IEEE DOI 1701
Feature extraction BibRef

Tommasi, T.[Tatiana], Lanzi, M.[Martina], Russo, P.[Paolo], Caputo, B.[Barbara],
Learning the Roots of Visual Domain Shift,
TASKCV16(III: 475-482).
Springer DOI 1611
Where domain adaption originates. BibRef

Yoo, D.[Donggeun], Kim, N.[Namil], Park, S.[Sunggyun], Paek, A.S.[Anthony S.], Kweon, I.S.[In So],
Pixel-Level Domain Transfer,
ECCV16(VIII: 517-532).
Springer DOI 1611
BibRef

Motiian, S.[Saeid], Piccirilli, M., Adjeroh, D.A., Doretto, G.[Gianfranco],
Information Bottleneck Learning Using Privileged Information for Visual Recognition,
CVPR16(1496-1505)
IEEE DOI 1612
BibRef

Motiian, S.[Saeid], Doretto, G.[Gianfranco],
Information Bottleneck Domain Adaptation with Privileged Information for Visual Recognition,
ECCV16(VII: 630-647).
Springer DOI 1611
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

Pilanci, M., Vural, E.,
Domain adaptation via transferring spectral properties of label functions on graphs,
IVMSP16(1-5)
IEEE DOI 1608
Coherence BibRef

Liu, S.[Siqi], Kovashka, A.[Adriana],
Adapting attributes by selecting features similar across domains,
WACV16(1-8)
IEEE DOI 1606
Adaptation models. Adapt attributes to different domain. BibRef

Xu, H.Y.[Hong-Yu], Zheng, J.J.[Jing-Jing], Chellappa, R.[Rama],
Bridging the Domain Shift by Domain Adaptive Dictionary Learning,
BMVC15(xx-yy).
DOI Link 1601
BibRef

Lin, Y.W.[Yue-Wei], Chen, J.[Jing], Cao, Y.[Yu], Zhou, Y.J.[You-Jie], Zhang, L.F.[Ling-Feng], Wang, S.[Song],
Cross-domain recognition by identifying compact joint subspaces,
ICIP15(3461-3465)
IEEE DOI 1512
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

Ranjan, V.[Viresh], Rasiwasia, N.[Nikhil], Jawahar, C.V.,
Multi-label Cross-Modal Retrieval,
ICCV15(4094-4102)
IEEE DOI 1602
Benchmark testing BibRef

Ranjan, V.[Viresh], Harit, G., Jawahar, C.V.,
Domain adaptation by aligning locality preserving subspaces,
ICAPR15(1-6)
IEEE DOI 1511
computer vision 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

Zhu, G.T.[Guang-Tang], Yang, H.F.[Han-Fang], Lin, L.[Lan], Zhou, G.C.[Gui-Chun], Zhou, X.D.[Xiang-Dong],
An Informative Logistic Regression for Cross-Domain Image Classification,
CVS15(147-156).
Springer DOI 1507
BibRef

Ranjan, V.[Viresh], Harit, G.[Gaurav], Jawahar, C.V.,
Learning Partially Shared Dictionaries for Domain Adaptation,
FSLCV14(III: 247-261).
Springer DOI 1504
BibRef

Csurka, G.[Gabriela], Chidlovskii, B.[Boris], Clinchant, S.,
Adapted Domain Specific Class Means,
TASKCV15(80-84)
IEEE DOI 1602
Adaptation models BibRef

Csurka, G.[Gabriela], Chidlovskii, B.[Boris], Perronnin, F.[Florent],
Domain Adaptation with a Domain Specific Class Means Classifier,
TASKCV14(32-46).
Springer DOI 1504
BibRef

Patricia, N.[Novi], Caputo, B.[Barbara],
Learning to Learn, from Transfer Learning to Domain Adaptation: A Unifying Perspective,
CVPR14(1442-1449)
IEEE DOI 1409
BibRef

Samanta, S., Selvan, A.T., Das, S.,
Cross-domain clustering performed by transfer of knowledge across domains,
NCVPRIPG13(1-4)
IEEE DOI 1408
iterative methods 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

Zheng, J.J.[Jing-Jing], Liu, M.Y.[Ming-Yu], Chellappa, R.[Rama], Phillips, P.J.[P. Jonathon],
A Grassmann manifold-based domain adaptation approach,
ICPR12(2095-2099).
WWW Link. 1302
shifts in the distribution between training and testing data 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

Mirrashed, F.[Fatemeh], Rastegari, M.[Mohammad],
Domain Adaptive Classification,
ICCV13(2608-2615)
IEEE DOI 1403
BibRef

Mirrashed, F., Morariu, V.I., Siddiquie, B., Feris, R.S., Davis, L.S.,
Domain adaptive object detection,
WACV13(323-330).
IEEE DOI 1303
transfer learning techniques BibRef

Jhuo, I.H.[I-Hong], Liu, D.[Dong], Lee, D.T., Chang, S.F.[Shih-Fu],
Robust visual domain adaptation with low-rank reconstruction,
CVPR12(2168-2175).
IEEE DOI 1208
BibRef

Vezhnevets, A.[Alexander], Buhmann, J.M.[Joachim M.],
Agnostic Domain Adaptation,
DAGM11(376-385).
Springer DOI 1109
Transfer learning. See also Towards weakly supervised semantic segmentation by means of multiple instance and multitask 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 .