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.[Yakoub],
Melgani, F.,
Alhichri, H.[Haikel],
Alajlan, N.[Naif],
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
Adayel, R.[Reham],
Bazi, Y.[Yakoub],
Alhichri, H.[Haikel],
Alajlan, N.[Naif],
Deep Open-Set Domain Adaptation for Cross-Scene Classification based
on Adversarial Learning and Pareto Ranking,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link
2006
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
Zhang, Z.[Zhen],
Wang, M.[Mianzhi],
Nehorai, A.[Arye],
Optimal Transport in Reproducing Kernel Hilbert Spaces:
Theory and Applications,
PAMI(42), No. 7, July 2020, pp. 1741-1754.
IEEE DOI
2006
Omparing and matching distributions in reproducing kernel Hilbert spaces.
Kernel, Covariance matrices, Hilbert space, Task analysis, Geometry,
Modeling, Optimal transport, reproducing kernel hilbert spaces,
domain adaptation
BibRef
Zhang, Z.[Zhen],
Wang, M.[Mianzhi],
Huang, Y.,
Nehorai, A.[Arye],
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
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
Hosseinzadeh, H.[Hamidreza],
Einalou, Z.[Zahra],
Logistic regression projection-based feature representation for visual
domain adaptation,
SIViP(14), No. 6, September 2020, pp. 1115-1123.
WWW Link.
2008
BibRef
Ishii, M.,
Takenouchi, T.,
Sugiyama, M.,
Partially Zero-shot Domain Adaptation from Incomplete Target Data
with Missing Classes,
WACV20(3041-3049)
IEEE DOI
2006
Training, Neural networks, Estimation, Standards, Feature extraction,
Surveillance, Optimization
BibRef
Kae, A.,
Song, Y.,
Image to Video Domain Adaptation Using Web Supervision,
WACV20(556-564)
IEEE DOI
2006
Adaptation models, Noise measurement, Training, Data models,
Task analysis
BibRef
Truong, D.T.[Dat T.],
Duong, C.N.[Chi Nhan],
Luu, K.[Khoa],
Tran, M.T.[Minh-Triet],
Le, N.[Ngan],
Domain Generalization via Universal Non-volume Preserving Approach,
CRV20(93-100)
IEEE DOI
2006
Digits, faces, pedestrians.
BibRef
Hsu, H.,
Yao, C.,
Tsai, Y.,
Hung, W.,
Tseng, H.,
Singh, M.,
Yang, M.,
Progressive Domain Adaptation for Object Detection,
WACV20(738-746)
IEEE DOI
2006
Task analysis, Object detection, Feature extraction,
Adaptation models, Training, Proposals, Testing
BibRef
Elshamli, A.,
Taylor, G.W.,
Areibi, S.,
Multisource Domain Adaptation for Remote Sensing Using Deep Neural
Networks,
GeoRS(58), No. 5, May 2020, pp. 3328-3340.
IEEE DOI
2005
Deep neural networks (DNNs), land-use classification,
Learning without Forgetting (LwF), local climate zones (LCZ),
transfer learning
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.C.[Rong-Chang],
Yu, F.[Fei],
Wang, J.C.[Jia-Chao],
Wang, Y.Z.[Yi-Zhou],
Zhang, L.[Li],
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
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.G.[Hong-Guang],
Harandi, M.T.[Mehrtash T.],
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 .