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
Kouw, W.M.,
Loog, M.[Marco],
A Review of Domain Adaptation without Target Labels,
PAMI(43), No. 3, March 2021, pp. 766-785.
IEEE DOI
2102
Sociology, Statistics, Machine learning, Pattern recognition,
Hospitals, Image recognition, Pattern analysis, Machine learning,
sample selection bias
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],
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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
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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
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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
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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
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
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
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
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
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.X.[Rui-Xi],
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
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
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
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
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
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.M.,
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
Ma, X.R.[Xiao-Rui],
Mou, X.R.[Xue-Rong],
Wang, J.[Jie],
Liu, X.K.[Xiao-Kai],
Geng, J.[Jie],
Wang, H.Y.[Hong-Yu],
Cross-Dataset Hyperspectral Image Classification Based on Adversarial
Domain Adaptation,
GeoRS(59), No. 5, May 2021, pp. 4179-4190.
IEEE DOI
2104
Hyperspectral imaging, Training, Generators, Task analysis,
Deep learning, Classification, cross-data set, domain adaptation, hyperspectral image
BibRef
Yang, F.,
Chang, J.,
Tsai, C.,
Wang, Y.F.,
A Multi-Domain and Multi-Modal Representation Disentangler for
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IP(29), 2020, pp. 2795-2807.
IEEE DOI
2001
Representation disentanglement, image translation,
domain adaptation, deep learning
BibRef
Yao, Y.[Yuan],
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Li, X.[Xutao],
Ye, Y.M.[Yun-Ming],
Discriminative distribution alignment:
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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],
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Object detection based on semi-supervised domain adaptation for
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MVA(31), No. 3, March 2020, pp. Article18.
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BibRef
Han, C.[Chao],
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Visual domain adaptation based on modified A-distance and sparse
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PR(104), 2020, pp. 107254.
Elsevier DOI
2005
Domain adaptation, distance, Sparse filtering
BibRef
Zhang, Z.[Zhen],
Wang, M.Z.[Mian-Zhi],
Nehorai, A.[Arye],
Optimal Transport in Reproducing Kernel Hilbert Spaces:
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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.Z.[Mian-Zhi],
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
Hosseinzadeh, H.[Hamidreza],
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Logistic regression projection-based feature representation for visual
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Karimpour, M.[Morvarid],
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Multi-source domain adaptation for image classification,
MVA(31), No. 6, August 2020, pp. Article44.
Springer DOI
2008
BibRef
Mancini, M.[Massimiliano],
Ricci, E.[Elisa],
Caputo, B.[Barbara],
Bulò, S.R.[Samuel Rota],
Boosting binary masks for multi-domain learning through affine
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MVA(31), No. 6, August 2020, pp. Article42.
Springer DOI
2008
BibRef
Mancini, M.[Massimiliano],
Akata, Z.[Zeynep],
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Caputo, B.[Barbara],
Towards Recognizing Unseen Categories in Unseen Domains,
ECCV20(XXIII:466-483).
Springer DOI
2011
BibRef
Chen, S.,
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Jin, X.,
Yang, X.,
Domain Adaptation by Joint Distribution Invariant Projections,
IP(29), 2020, pp. 8264-8277.
IEEE DOI
2008
Kernel, Covariance matrices, Training, Labeling, Estimation,
Optimization, Dimensionality reduction, L²-distance,
Riemannian optimization
BibRef
Kim, Y.,
Cho, D.,
Hong, S.,
Towards Privacy-Preserving Domain Adaptation,
SPLetters(27), 2020, pp. 1675-1679.
IEEE DOI
1806
Prototypes, Reliability, Adaptation models, Feature extraction,
Data models, Data privacy, Training, Domain adaptation,
class prototypes
BibRef
Tian, L.,
Tang, Y.,
Hu, L.,
Ren, Z.,
Zhang, W.,
Domain Adaptation by Class Centroid Matching and Local Manifold
Self-Learning,
IP(29), 2020, pp. 9703-9718.
IEEE DOI
2011
Manifolds, Proposals, Prototypes, Optimization,
Toy manufacturing industry, Convergence, Benchmark testing,
local manifold self-learning
BibRef
Zhang, J.,
Liu, J.,
Pan, B.,
Shi, Z.,
Domain Adaptation Based on Correlation Subspace Dynamic Distribution
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GeoRS(58), No. 11, November 2020, pp. 7920-7930.
IEEE DOI
2011
Remote sensing, Feature extraction, Correlation, Semantics, Training,
Testing, Task analysis, Data shift, distribution alignment,
remote sensing image scene classification
BibRef
Wang, Y.[Yuxi],
Zhang, Z.X.[Zhao-Xiang],
Hao, W.L.[Wang-Li],
Song, C.F.[Chun-Feng],
Attention Guided Multiple Source and Target Domain Adaptation,
IP(30), 2021, pp. 892-906.
IEEE DOI
2012
Semantics, Task analysis, Generators,
Generative adversarial networks, Feature extraction,
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BibRef
Deng, W.,
Zheng, L.,
Sun, Y.,
Jiao, J.,
Rethinking Triplet Loss for Domain Adaptation,
CirSysVideo(31), No. 1, January 2021, pp. 29-37.
IEEE DOI
2101
Semantics, Feature extraction, Measurement, Adaptation models,
Data models, Image color analysis, Sun, Domain adaptation,
semantic alignment
BibRef
Wang, W.[Wei],
Chen, S.L.[Sheng-Lun],
Xiang, Y.K.[Yuan-Kai],
Sun, J.[Jing],
Li, H.J.[Hao-Jie],
Wang, Z.H.[Zhi-Hui],
Sun, F.[Fuming],
Ding, Z.M.[Zheng-Ming],
Li, B.[Baopu],
Sparsely-labeled source assisted domain adaptation,
PR(112), 2021, pp. 107803.
Elsevier DOI
2102
Domain adaptation, Sparsely-labeled source,
Semi-supervised clustering, Label propagation
BibRef
Lee, W.[Woojin],
Kim, H.[Hoki],
Lee, J.W.[Jae-Wook],
Compact class-conditional domain invariant learning for multi-class
domain adaptation,
PR(112), 2021, pp. 107763.
Elsevier DOI
2102
Domain adaptation, Generalization bound,
Class-conditional domain invariant learning, Transfer Learning
BibRef
Wang, W.[Wei],
Wang, H.[Hao],
Ran, Z.Y.[Zhi-Yong],
He, R.[Ran],
Learning Robust Feature Transformation for Domain Adaptation,
PR(114), 2021, pp. 107870.
Elsevier DOI
2103
Domain adaptation, Linear transformation, Correntropy,
Kernel mean p-power error loss, Half-quadratic optimization
BibRef
Zhang, W.,
Xu, D.,
Zhang, J.,
Ouyang, W.,
Progressive Modality Cooperation for Multi-Modality Domain Adaptation,
IP(30), 2021, pp. 3293-3306.
IEEE DOI
2103
Visualization, Image recognition, Semantics, Collaboration,
Reliability, Object recognition, Task analysis, Domain adaptation,
learning using privileged information (LUPI)
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Niu, C.[Chang],
Shang, J.[Junyuan],
Zhou, Z.H.[Zhi-Heng],
Huang, J.C.[Jun-Chu],
Wang, T.L.[Tian-Lei],
Li, X.W.[Xiang-Wei],
Common-specific feature learning for multi-source domain adaptation,
IET-IPR(14), No. 16, 19 December 2020, pp. 4049-4058.
DOI Link
2103
BibRef
Zuo, Y.,
Yao, H.,
Xu, C.,
Attention-Based Multi-Source Domain Adaptation,
IP(30), 2021, pp. 3793-3803.
IEEE DOI
2104
Correlation, Adaptation models, Feature extraction,
Target recognition, Data models, Transfer learning, Visualization,
weighted moment distance
BibRef
Kurmi, V.K.[Vinod K.],
Subramanian, V.K.[Venkatesh K.],
Namboodiri, V.P.[Vinay P.],
Informative discriminator for domain adaptation,
IVC(111), 2021, pp. 104180.
Elsevier DOI
2106
CNN, Domain adaptation, Adversarial learning, Discriminator,
Ensemble method, Object recognition
BibRef
Wang, J.H.[Jing-Hua],
Cheng, M.M.[Ming-Ming],
Jiang, J.M.[Jian-Min],
Domain Shift Preservation for Zero-Shot Domain Adaptation,
IP(30), 2021, pp. 5505-5517.
IEEE DOI
2106
BibRef
Earlier: A1, A3, Only:
Adversarial Learning for Zero-shot Domain Adaptation,
ECCV20(XXI:329-344).
Springer DOI
2011
BibRef
Earlier: A1, A3, Only:
Conditional Coupled Generative Adversarial Networks for Zero-Shot
Domain Adaptation,
ICCV19(3374-3383)
IEEE DOI
2004
Task analysis, Data models, Generative adversarial networks,
Training, Feature extraction, Adaptation models, Semantics,
adversarial learning.
computer vision, image classification,
learning (artificial intelligence), neural nets, CoCoGAN,
BibRef
Meher, S.K.[Saroj K.],
Kothari, N.S.[Neeta S.],
Interpretable Rule-Based Fuzzy ELM and Domain Adaptation for Remote
Sensing Image Classification,
GeoRS(59), No. 7, July 2021, pp. 5907-5919.
IEEE DOI
2106
Adaptation models, Feature extraction, Indexes, Task analysis,
Computer architecture, Data models, Training, remote sensing
BibRef
Schrom, S.[Sebastian],
Hasler, S.[Stephan],
Adamy, J.[Jürgen],
Improved multi-source domain adaptation by preservation of factors,
IVC(112), 2021, pp. 104209.
Elsevier DOI
2107
Domain adaptation, Multi-source domain adaptation,
Adversarial domain adaptation, Negative transfer, Domain factors
BibRef
Wu, H.R.[Han-Rui],
Zhu, H.[Hong],
Yan, Y.[Yuguang],
Wu, J.J.[Jia-Ju],
Zhang, Y.F.[Yi-Fan],
Ng, M.K.[Michael K.],
Heterogeneous Domain Adaptation by Information Capturing and
Distribution Matching,
IP(30), 2021, pp. 6364-6376.
IEEE DOI
2107
Image reconstruction, Training, Support vector machines,
Loss measurement, Correlation, Data models, Transfer learning,
generalized gradient flow
BibRef
Wu, X.P.[Xiao-Ping],
Chang, J.L.[Jian-Long],
Lai, Y.K.[Yu-Kun],
Yang, J.F.[Ju-Feng],
Tian, Q.[Qi],
BiSPL: Bidirectional Self-Paced Learning for Recognition From Web
Data,
IP(30), 2021, pp. 6512-6527.
IEEE DOI
2108
Training, Task analysis, Noise measurement, Image recognition,
Visualization, Knowledge engineering, Adaptation models, noisy web data
BibRef
Shamsolmoali, P.[Pourya],
García, S.[Salvador],
Zhou, H.Y.[Hui-Yu],
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Advances in domain adaptation for computer vision,
IVC(114), 2021, pp. 104268.
Elsevier DOI
2109
BibRef
Eguchi, S.[Shu],
Nakamura, R.[Ryo],
Tanaka, M.[Masaru],
Output augmentation works well without any domain knowledge,
MVA21(1-5)
DOI Link
2109
To improve generalization performance, without requiring data augmentation.
Training data, Task analysis, Image classification
BibRef
Kishida, I.[Ikki],
Chen, H.[Hong],
Baba, M.[Masaki],
Jin, J.[Jiren],
Amma, A.[Ayako],
Nakayama, H.[Hideki],
Object Recognition with Continual Open Set Domain Adaptation for Home
Robot,
WACV21(1516-1525)
IEEE DOI
2106
Training, Learning systems,
Object detection, Detectors, Search problems
BibRef
Kurmi, V.K.[Vinod K.],
Subramanian, V.K.[Venkatesh K.],
Namboodiri, V.P.[Vinay P.],
Domain Impression: A Source Data Free Domain Adaptation Method,
WACV21(615-625)
IEEE DOI
2106
Adaptation models, Data privacy,
Analytical models, Computational modeling, Memory management
BibRef
Tranheden, W.[Wilhelm],
Olsson, V.[Viktor],
Pinto, J.[Juliano],
Svensson, L.[Lennart],
DACS: Domain Adaptation via Cross-domain Mixed Sampling,
WACV21(1378-1388)
IEEE DOI
2106
Training, Adaptation models, Image segmentation, Systematics,
Semantics, Benchmark testing
BibRef
Zhao, A.[An],
Ding, M.Y.[Ming-Yu],
Lu, Z.W.[Zhi-Wu],
Xiang, T.[Tao],
Niu, Y.[Yulei],
Guan, J.[Jiechao],
Wen, J.R.[Ji-Rong],
Domain-Adaptive Few-Shot Learning,
WACV21(1389-1398)
IEEE DOI
2106
Training, Measurement, Bridges, Adaptation models,
Computational modeling
BibRef
Wang, F.[Fei],
Ding, Y.D.[You-Dong],
Liang, H.[Huan],
Wen, J.[Jing],
Discriminative and Selective Pseudo-labeling for Domain Adaptation,
MMMod21(I:365-377).
Springer DOI
2106
BibRef
Kim, Y.[Yoonhyung],
Kim, C.[Changick],
Semi-Supervised Domain Adaptation via Selective Pseudo Labeling and
Progressive Self-Training,
ICPR21(1059-1066)
IEEE DOI
2105
Training, Semantics, Object detection, Pattern recognition,
Noise robustness, Noise measurement, Reliability
BibRef
Kheirandishfard, M.[Mohsen],
Zohrizadeh, F.[Fariba],
Kamangar, F.[Farhad],
Class Conditional Alignment for Partial Domain Adaptation,
ICPR21(811-818)
IEEE DOI
2105
Adaptation models, Handheld computers, Transfer learning,
Benchmark testing, Pattern recognition, Task analysis, Standards
BibRef
Madasu, A.[Avinash],
Vijjini, A.R.[Anvesh Rao],
Sequential Domain Adaptation through Elastic Weight Consolidation for
Sentiment Analysis,
ICPR21(4879-4886)
IEEE DOI
2105
Training, Adaptation models, Sentiment analysis,
Computational modeling, Neural networks, Training data, Computer architecture
BibRef
Morsing, L.H.[Lukas Hedegaard],
Sheikh-Omar, O.A.[Omar Ali],
Iosifidis, A.[Alexandros],
Supervised Domain Adaptation using Graph Embedding,
ICPR21(7841-7847)
IEEE DOI
2105
Dimensionality reduction, Transfer learning, Neural networks,
Training data, Benchmark testing, Eigenvalues and eigenfunctions,
Convolutional neural networks
BibRef
Xiao, L.[Liang],
Xu, J.[Jiaolong],
Zhao, D.[Dawei],
Wang, Z.[Zhiyu],
Wang, L.[Li],
Nie, Y.M.[Yi-Ming],
Dai, B.[Bin],
Self-Supervised Domain Adaptation with Consistency Training,
ICPR21(6874-6880)
IEEE DOI
2105
Training, Force, Benchmark testing, Pattern recognition,
Object recognition, Task analysis, Image classification
BibRef
Borlino, F.C.[Francesco Cappio],
D'Innocente, A.[Antonio],
Tommasi, T.[Tatiana],
Rethinking Domain Generalization Baselines,
ICPR21(9227-9233)
IEEE DOI
2105
Deep learning, Writing, Tools, Robustness, Data models,
Pattern recognition, Standards
BibRef
Wang, F.[Fei],
Ding, Y.D.[You-Dong],
Liang, H.[Huan],
Gao, Y.Z.[Yu-Zhen],
Che, W.Q.[Wen-Qi],
A Unified Framework for Distance-Aware Domain Adaptation,
ICPR21(1796-1803)
IEEE DOI
2105
Manifolds, Geometry, Adaptation models, Data mining
BibRef
Wang, Z.[Ziqi],
Loog, M.[Marco],
van Gemert, J.C.[Jan C.],
Respecting Domain Relations: Hypothesis Invariance for Domain
Generalization,
ICPR21(9756-9763)
IEEE DOI
2105
Training, Estimation, Distributed databases, Probabilistic logic,
Pattern recognition, Reliability, Domain generalization,
invariant representation
BibRef
Wei, X.,
Liu, S.,
Wang, C.,
Xiang, Y.,
Qiao, X.,
Duan, Z.,
Zhao, C.,
Lu, Y.,
Class-unbalanced domain adaptation for object detection via dynamic
weighting mechanism,
3DV20(495-503)
IEEE DOI
2102
Object detection, Adaptation models, Training, Feature extraction,
Neural networks, Convolution, Detectors, domain adaptation,
partial domain adaptation
BibRef
Shin, I.[Inkyu],
Woo, S.[Sanghyun],
Pan, F.[Fei],
Kweon, I.S.[In So],
Two-phase Pseudo Label Densification for Self-training Based Domain
Adaptation,
ECCV20(XIII:532-548).
Springer DOI
2011
BibRef
Liang, J.[Jian],
Wang, Y.B.[Yun-Bo],
Hu, D.P.[Da-Peng],
He, R.[Ran],
Feng, J.S.[Jia-Shi],
A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation,
ECCV20(XI:123-140).
Springer DOI
2011
BibRef
Fu, B.[Bo],
Cao, Z.J.[Zhang-Jie],
Long, M.S.[Ming-Sheng],
Wang, J.M.[Jian-Min],
Learning to Detect Open Classes for Universal Domain Adaptation,
ECCV20(XV:567-583).
Springer DOI
2011
BibRef
Seo, S.[Seonguk],
Suh, Y.[Yumin],
Kim, D.[Dongwan],
Kim, G.[Geeho],
Han, J.W.[Jong-Woo],
Han, B.H.[Bo-Hyung],
Learning to Optimize Domain Specific Normalization for Domain
Generalization,
ECCV20(XXII:68-83).
Springer DOI
2011
BibRef
Wallace, B.[Bram],
Hariharan, B.[Bharath],
Extending and Analyzing Self-supervised Learning Across Domains,
ECCV20(XXVI:717-734).
Springer DOI
2011
BibRef
Yang, L.[Luyu],
Balaji, Y.[Yogesh],
Lim, S.N.[Ser-Nam],
Shrivastava, A.[Abhinav],
Curriculum Manager for Source Selection in Multi-source Domain
Adaptation,
ECCV20(XIV:608-624).
Springer DOI
2011
BibRef
Kim, T.[Taekyung],
Kim, C.[Changick],
Attract, Perturb, and Explore: Learning a Feature Alignment Network for
Semi-supervised Domain Adaptation,
ECCV20(XIV:591-607).
Springer DOI
2011
BibRef
Jin, Y.[Ying],
Wang, X.[Ximei],
Long, M.S.[Ming-Sheng],
Wang, J.M.[Jian-Min],
Minimum Class Confusion for Versatile Domain Adaptation,
ECCV20(XXI:464-480).
Springer DOI
2011
BibRef
Sasagawa, Y.[Yukihiro],
Nagahara, H.[Hajime],
Yolo in the Dark: Domain Adaptation Method for Merging Multiple Models,
ECCV20(XXI:345-359).
Springer DOI
2011
BibRef
Du, Y.J.[Ying-Jun],
Xu, J.[Jun],
Xiong, H.[Huan],
Qiu, Q.A.[Qi-Ang],
Zhen, X.T.[Xian-Tong],
Snoek, C.G.M.[Cees G. M.],
Shao, L.[Ling],
Learning to Learn with Variational Information Bottleneck for Domain
Generalization,
ECCV20(X:200-216).
Springer DOI
2011
BibRef
Chattopadhyay, P.[Prithvijit],
Balaji, Y.[Yogesh],
Hoffman, J.[Judy],
Learning to Balance Specificity and Invariance for In and Out of Domain
Generalization,
ECCV20(IX:301-318).
Springer DOI
2011
BibRef
Wang, S.J.[Shu-Jun],
Yu, L.[Lequan],
Li, C.[Caizi],
Fu, C.W.[Chi-Wing],
Heng, P.A.[Pheng-Ann],
Learning from Extrinsic and Intrinsic Supervisions for Domain
Generalization,
ECCV20(IX:159-176).
Springer DOI
2011
BibRef
Menapace, W.[Willi],
Lathuilière, S.[Stéphane],
Ricci, E.[Elisa],
Learning to Cluster Under Domain Shift,
ECCV20(XXVIII:736-752).
Springer DOI
2011
BibRef
Li, D.[Da],
Hospedales, T.M.[Timothy M.],
Online Meta-learning for Multi-source and Semi-supervised Domain
Adaptation,
ECCV20(XVI: 382-403).
Springer DOI
2010
BibRef
Gu, X.,
Sun, J.,
Xu, Z.,
Spherical Space Domain Adaptation With Robust Pseudo-Label Loss,
CVPR20(9098-9107)
IEEE DOI
2008
Robustness, Feature extraction, Mixture models, Entropy, Training,
Data models, Labeling
BibRef
Qiao, F.,
Zhao, L.,
Peng, X.,
Learning to Learn Single Domain Generalization,
CVPR20(12553-12562)
IEEE DOI
2008
Training, Task analysis, Transportation, Adaptation models,
Robustness, Perturbation methods, Measurement
BibRef
Kundu, J.N.[Jogendra Nath],
Venkat, N.[Naveen],
Rahul, M.V.,
Babu, R.V.[R. Venkatesh],
Universal Source-Free Domain Adaptation,
CVPR20(4543-4552)
IEEE DOI
2008
Adaptation models, Procurement, Data models, Training, Reliability,
Real-time systems, Animals
BibRef
Jamal, M.A.,
Brown, M.,
Yang, M.,
Wang, L.,
Gong, B.,
Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition
From a Domain Adaptation Perspective,
CVPR20(7607-7616)
IEEE DOI
2008
Training, Visualization, Adaptation models, Training data,
Machine learning, Data models, Head
BibRef
Yang, Y.,
Lao, D.,
Sundaramoorthi, G.,
Soatto, S.,
Phase Consistent Ecological Domain Adaptation,
CVPR20(9008-9017)
IEEE DOI
2008
Semantics, Image segmentation, Fourier transforms, Task analysis,
Training, Adaptation models, Visualization
BibRef
He, G.,
Liu, X.,
Fan, F.,
You, J.,
Classification-aware Semi-supervised Domain Adaptation,
MULWS20(4147-4156)
IEEE DOI
2008
Training, Emotion recognition, Visualization,
Task analysis, Reliability, Training data
BibRef
Huang, F.,
Zhang, L.,
Yang, Y.,
Zhou, X.,
Probability Weighted Compact Feature for Domain Adaptive Retrieval,
CVPR20(9579-9588)
IEEE DOI
2008
Binary codes, Correlation, Image retrieval, Manifolds, Training, Bayes methods
BibRef
Chen, Z.,
Chen, C.,
Cheng, Z.,
Jiang, B.,
Fang, K.,
Jin, X.,
Selective Transfer With Reinforced Transfer Network for Partial
Domain Adaptation,
CVPR20(12703-12711)
IEEE DOI
2008
Handheld computers, Generators, Feature extraction, Training,
Adaptation models, Image reconstruction, Learning (artificial intelligence)
BibRef
Liu, Z.,
Miao, Z.,
Pan, X.,
Zhan, X.,
Lin, D.,
Yu, S.X.,
Gong, B.,
Open Compound Domain Adaptation,
CVPR20(12403-12412)
IEEE DOI
2008
Compounds, Memory modules, Adaptation models, Feature extraction,
Meteorology, Training, Data models
BibRef
Xu, C.,
Zhao, X.,
Jin, X.,
Wei, X.,
Exploring Categorical Regularization for Domain Adaptive Object
Detection,
CVPR20(11721-11730)
IEEE DOI
2008
Training, Object detection, Detectors, Proposals, Feature extraction,
Adaptation models, Heating systems
BibRef
Zhang, Y.,
Davison, B.D.,
Impact of ImageNet Model Selection on Domain Adaptation,
WACVWS20(173-182)
IEEE DOI
2006
Feature extraction, Adaptation models, Neural networks,
Benchmark testing, Correlation, Data models, Task analysis
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
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
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
Saito, K.,
Kim, D.,
Sclaroff, S.,
Darrell, T.J.,
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
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
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
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
Li, D.,
Zhang, J.,
Yang, Y.,
Liu, C.,
Song, Y.,
Hospedales, T.M.,
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
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
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
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
Bapat, A.[Akash],
Frahm, J.M.[Jan-Michael],
The Domain Transform Solver,
CVPR19(6007-6016).
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
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
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.A.F.[Yu-Chi-Ang 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
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
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
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
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
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
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
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
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
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
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
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],
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
Unsupervised Domain Adaptation .