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],
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)
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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
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
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
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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
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,
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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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],
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Object detection based on semi-supervised domain adaptation for
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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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Boosting binary masks for multi-domain learning through affine
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Springer DOI
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Roy, S.[Subhankar],
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Zhong, Z.[Zhun],
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Curriculum Graph Co-Teaching for Multi-Target Domain Adaptation,
CVPR21(5347-5356)
IEEE DOI
2111
Deep learning, Computer network reliability, PROM,
Collaboration, Pattern recognition, Reliability
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Mancini, M.[Massimiliano],
Akata, Z.[Zeynep],
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Towards Recognizing Unseen Categories in Unseen Domains,
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Springer DOI
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Chen, S.,
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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
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Tian, L.,
Tang, Y.,
Hu, L.,
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Domain Adaptation by Class Centroid Matching and Local Manifold
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IP(29), 2020, pp. 9703-9718.
IEEE DOI
2011
Manifolds, Proposals, Prototypes, Optimization,
Toy manufacturing industry, Convergence, Benchmark testing,
local manifold self-learning
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Zhang, J.,
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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
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Wang, Y.X.[Yu-Xi],
Zhang, Z.X.[Zhao-Xiang],
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IP(30), 2021, pp. 892-906.
IEEE DOI
2012
Semantics, Task analysis, Generators,
Generative adversarial networks, Feature extraction,
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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],
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Ding, Z.M.[Zheng-Ming],
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PR(112), 2021, pp. 107803.
Elsevier DOI
2102
Domain adaptation, Sparsely-labeled source,
Semi-supervised clustering, Label propagation
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He, R.[Ran],
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PR(114), 2021, pp. 107870.
Elsevier DOI
2103
Domain adaptation, Linear transformation, Correntropy,
Kernel mean p-power error loss, Half-quadratic optimization
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Zhang, W.,
Xu, D.,
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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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Kurmi, V.K.[Vinod K.],
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Informative discriminator for domain adaptation,
IVC(111), 2021, pp. 104180.
Elsevier DOI
2106
CNN, Domain adaptation, Adversarial learning, Discriminator,
Ensemble method, Object recognition
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Wang, J.H.[Jing-Hua],
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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.
image classification,
learning (artificial intelligence), neural nets, CoCoGAN,
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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,
Data models, Training, remote sensing
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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
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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
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Shamsolmoali, P.[Pourya],
García, S.[Salvador],
Zhou, H.Y.[Hui-Yu],
Celebi, M.E.[M. Emre],
Advances in domain adaptation for computer vision,
IVC(114), 2021, pp. 104268.
Elsevier DOI
2109
BibRef
Zhou, K.Y.[Kai-Yang],
Yang, Y.X.[Yong-Xin],
Qiao, Y.[Yu],
Xiang, T.[Tao],
Domain Adaptive Ensemble Learning,
IP(30), 2021, pp. 8008-8018.
IEEE DOI
2109
Adaptation models, Training, Collaboration, Feature extraction,
Computational modeling, Head, Neural networks, Domain adaptation,
collaborative ensemble learning
BibRef
Wittich, D.,
Rottensteiner, F.,
Appearance based deep domain adaptation for the classification of
aerial images,
PandRS(180), 2021, pp. 82-102.
Elsevier DOI
2109
Domain Adaptation, Pixel-wise Classification, Deep Learning,
Aerial Images, Remote Sensing, Appearance Adaptation
BibRef
Hedegaard, L.[Lukas],
Sheikh-Omar, O.A.[Omar Ali],
Iosifidis, A.[Alexandros],
Supervised Domain Adaptation: A Graph Embedding Perspective and a
Rectified Experimental Protocol,
IP(30), 2021, pp. 8619-8631.
IEEE DOI
2110
Training, Task analysis, Protocols, Feature extraction,
Nanoelectromechanical systems, Transfer learning, Deep learning, domain shift
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Wang, Q.[Qian],
Breckon, T.P.[Toby P.],
Cross-domain structure preserving projection for heterogeneous domain
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PR(123), 2022, pp. 108362.
Elsevier DOI
2112
Heterogeneous domain adaptation, Cross-domain projection,
Image classification, Text classification
BibRef
Gong, T.F.[Teng-Fei],
Zheng, X.T.[Xiang-Tao],
Lu, X.Q.[Xiao-Qiang],
Cross-Domain Scene Classification by Integrating Multiple Incomplete
Sources,
GeoRS(59), No. 12, December 2021, pp. 10035-10046.
IEEE DOI
2112
Feature extraction, Optics, Distributed databases,
Adversarial machine learning, Technological innovation, Sensors,
unknown categories
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Yang, X.[Xu],
Deng, C.[Cheng],
Liu, T.L.[Tong-Liang],
Tao, D.C.[Da-Cheng],
Heterogeneous Graph Attention Network for Unsupervised
Multiple-Target Domain Adaptation,
PAMI(44), No. 4, April 2022, pp. 1992-2003.
IEEE DOI
2203
Semantics, Feature extraction, Adaptation models, Training,
Task analysis, Machine learning, Data models,
graph attention network
BibRef
Sun, J.[Jing],
Wang, Z.H.[Zhi-Hui],
Wang, W.[Wei],
Li, H.J.[Hao-Jie],
Sun, F.M.[Fu-Ming],
Ding, Z.M.[Zheng-Ming],
Joint Adaptive Dual Graph and Feature Selection for Domain Adaptation,
CirSysVideo(32), No. 3, March 2022, pp. 1453-1466.
IEEE DOI
2203
Manifolds, Feature extraction, Noise measurement,
Knowledge transfer, Probability distribution, Sun, adaptive dual graph
BibRef
Zuo, Y.K.[Yu-Kun],
Yao, H.T.[Han-Tao],
Zhuang, L.S.[Lian-Sheng],
Xu, C.S.[Chang-Sheng],
Seek Common Ground While Reserving Differences:
A Model-Agnostic Module for Noisy Domain Adaptation,
MultMed(24), 2022, pp. 1020-1030.
IEEE DOI
2203
Noise measurement, Adaptation models, Predictive models,
Reliability, Task analysis, Standards, Data models,
Reserve differences component
BibRef
Gao, Y.[Yuan],
Chen, P.[Peipeng],
Gao, Y.[Yue],
Wang, J.P.[Jin-Peng],
Pan, Y.[YoungSun],
Ma, A.J.[Andy J.],
Hierarchical feature disentangling network for universal domain
adaptation,
PR(127), 2022, pp. 108616.
Elsevier DOI
2205
Universal domain adaptation, Feature disentanglement,
Domain adversarial training, Sample reweighting
BibRef
Kutbi, M.[Mohammed],
Peng, K.C.[Kuan-Chuan],
Wu, Z.[Ziyan],
Zero-Shot Deep Domain Adaptation With Common Representation Learning,
PAMI(44), No. 7, July 2022, pp. 3909-3924.
IEEE DOI
2206
Task analysis, Measurement, Training data, Training, Sensor fusion,
Semantics, Faces, Zero-shot, domain adaptation, sensor fusion,
metric learning
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
Wang, W.[Wei],
Li, B.[Baopu],
Wang, M.Z.[Meng-Zhu],
Nie, F.P.[Fei-Ping],
Wang, Z.H.[Zhi-Hui],
Li, H.J.[Hao-Jie],
Confidence Regularized Label Propagation Based Domain Adaptation,
CirSysVideo(32), No. 6, June 2022, pp. 3319-3333.
IEEE DOI
2206
Entropy, Propagation losses, Labeling, Integrated circuit modeling,
Dispersion, Data models, Adaptation models, Domain adaptation,
label-induced losses
BibRef
Tian, J.Y.[Jia-Yi],
Zhang, J.[Jing],
Li, W.[Wen],
Xu, D.[Dong],
VDM-DA: Virtual Domain Modeling for Source Data-Free Domain
Adaptation,
CirSysVideo(32), No. 6, June 2022, pp. 3749-3760.
IEEE DOI
2206
Data models, Adaptation models, Task analysis, Feature extraction,
Training, Entropy, Transfer learning, source data-free
BibRef
Deng, Z.Y.[Zhong-Ying],
Zhou, K.Y.[Kai-Yang],
Li, D.[Da],
He, J.J.[Jun-Jun],
Song, Y.Z.[Yi-Zhe],
Xiang, T.[Tao],
Dynamic Instance Domain Adaptation,
IP(31), 2022, pp. 4585-4597.
IEEE DOI
2207
Adaptation models, Painting,
Picture archiving and communication systems,
dynamic instance domain adaptation
BibRef
Balgi, S.[Sourabh],
Dukkipati, A.[Ambedkar],
Contradistinguisher:
A Vapnik's Imperative to Unsupervised Domain Adaptation,
PAMI(44), No. 9, September 2022, pp. 4730-4747.
IEEE DOI
2208
Graphics processing units, Task analysis, Standards,
Adaptation models, Neural networks, Data models, Training,
unsupervised learning
BibRef
Liu, C.[Chang],
Zhou, L.H.[Li-Hua],
Ye, M.[Mao],
Li, X.[Xue],
Self-Alignment for Black-Box Domain Adaptation of Image
Classification,
SPLetters(29), 2022, pp. 1709-1713.
IEEE DOI
2208
Adaptation models, Feature extraction, Data models,
Image classification, Training, Predictive models, Noise reduction
BibRef
Saltori, C.[Cristiano],
Rota, P.[Paolo],
Sebe, N.[Nicu],
Almeida, J.[Jurandy],
Low-budget label query through domain alignment enforcement,
CVIU(222), 2022, pp. 103485.
Elsevier DOI
2209
Manually label only a small subset of samples.
Low-budget data labeling, Unsupervised domain adaptation
BibRef
Azizzadenesheli, K.[Kamyar],
Importance Weight Estimation and Generalization in Domain Adaptation
Under Label Shift,
PAMI(44), No. 10, October 2022, pp. 6578-6584.
IEEE DOI
2209
Diseases, Task analysis, Hilbert space, Statistics, Sociology,
Predictive models, Medical diagnostic imaging, machine learning
BibRef
Wang, S.S.[Shan-Shan],
Zhang, L.[Lei],
Wang, P.C.[Pi-Chao],
Wang, M.Z.[Meng-Zhu],
Zhang, X.Y.[Xing-Yi],
BP-triplet net for unsupervised domain adaptation:
A Bayesian perspective,
PR(133), 2023, pp. 108993.
Elsevier DOI
2210
Cross domain class alignment, Unsupervised domain adaptation,
Metric learning, Bayesian perspective
BibRef
Luo, M.K.[Ming-Kai],
Yang, Z.[Zhao],
Ai, W.W.[Wei-Wei],
Liu, J.H.[Jie-Hao],
Confidence based class weight and embedding discrepancy constraint
network for partial domain adaptation,
JVCIR(88), 2022, pp. 103630.
Elsevier DOI
2210
Partial domain adaptation, Deep transfer learning,
Adversarial alignment, Classification learning
BibRef
Zhang, L.[Lei],
Zuo, L.Y.[Li-Yun],
Wang, B.Y.[Bao-Yan],
Li, X.[Xin],
Zhen, X.T.[Xian-Tong],
Variational Hyperparameter Inference for Few-Shot Learning Across
Domains,
CirSysVideo(32), No. 11, November 2022, pp. 7448-7459.
IEEE DOI
2211
Task analysis, Adaptation models, Optimization, Data models,
Uncertainty, Training, Probabilistic logic, Meta learning,
variational inference
BibRef
Luo, L.K.[Ling-Kun],
Chen, L.M.[Li-Ming],
Hu, S.Q.[Shi-Qiang],
Attention Regularized Laplace Graph for Domain Adaptation,
IP(31), 2022, pp. 7322-7337.
IEEE DOI
2212
Manifolds, Manifold learning, Task analysis, Data models, Training,
Faces, Data structures, Domain adaptation (DA),
manifold learning
BibRef
Fang, Z.[Zhen],
Lu, J.[Jie],
Liu, F.[Feng],
Zhang, G.Q.[Guang-Quan],
Semi-Supervised Heterogeneous Domain Adaptation:
Theory and Algorithms,
PAMI(45), No. 1, January 2023, pp. 1087-1105.
IEEE DOI
2212
Classification algorithms, Task analysis, Kernel, Training data,
Training, Picture archiving and communication systems, Manifolds,
classification
BibRef
Ren, Y.[Yu],
Cong, Y.[Yang],
Dong, J.H.[Jia-Hua],
Sun, G.[Gan],
Uni3DA: Universal 3D Domain Adaptation for Object Recognition,
CirSysVideo(33), No. 1, January 2023, pp. 379-392.
IEEE DOI
2301
Point cloud compression, Training, Task analysis, Cognition, Robots,
Semantics, Unsupervised learning, transfer learning, 3D point cloud
BibRef
Gao, Z.[Zhi],
Wu, Y.W.[Yu-Wei],
Harandi, M.[Mehrtash],
Jia, Y.D.[Yun-De],
Curvature-Adaptive Meta-Learning for Fast Adaptation to Manifold Data,
PAMI(45), No. 2, February 2023, pp. 1545-1562.
IEEE DOI
2301
Manifolds, Task analysis, Optimization, Neural networks,
Adaptation models, Geometry, Data models, Meta-learning, curvature
BibRef
Maggio, S.[Simona],
Bouvier, V.[Victor],
Dreyfus-Schmidt, L.[Léo],
Performance Prediction Under Dataset Shift,
ICPR22(2466-2474)
IEEE DOI
2212
WWW Link. Measurement, Training, Video games, Uncertainty, Estimation,
Training data, Production
BibRef
Pérez, G.[Gustavo],
Maji, S.[Subhransu],
Domain Adaptors for Hyperspectral Images,
ICPR22(3048-3055)
IEEE DOI
2212
Training, Adaptation models, Color,
Benchmark testing, Semisupervised learning, Transformers
BibRef
Sadhukhan, R.[Ranajoy],
Chatterjee, A.[Ankita],
Mukhopadhyay, J.[Jayanta],
Patra, A.[Amit],
Taxonomy Driven Learning of Semantic Hierarchy of Classes,
ICIP22(171-175)
IEEE DOI
2211
Degradation, Visualization, Semantics, Taxonomy,
Convolutional neural networks, Task analysis,
agglomerative hierarchical clustering
BibRef
Jia, M.L.[Meng-Lin],
Tang, L.[Luming],
Chen, B.C.[Bor-Chun],
Cardie, C.[Claire],
Belongie, S.[Serge],
Hariharan, B.[Bharath],
Lim, S.N.[Ser-Nam],
Visual Prompt Tuning,
ECCV22(XXXIII:709-727).
Springer DOI
2211
WWW Link. Adapt pre-trainted model
BibRef
Choi, S.[Sungha],
Yang, S.[Seunghan],
Choi, S.[Seokeon],
Yun, S.[Sungrack],
Improving Test-Time Adaptation Via Shift-Agnostic Weight Regularization
and Nearest Source Prototypes,
ECCV22(XXXIII:440-458).
Springer DOI
2211
BibRef
Xu, C.F.[Chen-Feng],
Li, T.[Tian],
Tang, C.[Chen],
Sun, L.F.[Ling-Feng],
Keutzer, K.[Kurt],
Tomizuka, M.[Masayoshi],
Fathi, A.[Alireza],
Zhan, W.[Wei],
PreTraM: Self-supervised Pre-training via Connecting Trajectory and Map,
ECCV22(XXIX:34-50).
Springer DOI
2211
BibRef
Gowda, S.N.[Shreyank N.],
Rohrbach, M.[Marcus],
Keller, F.[Frank],
Sevilla-Lara, L.[Laura],
Learn2Augment:
Learning to Composite Videos for Data Augmentation in Action Recognition,
ECCV22(XXXI:242-259).
Springer DOI
2211
BibRef
Leng, Z.Q.[Zhao-Qi],
Cheng, S.[Shuyang],
Caine, B.[Benjamin],
Wang, W.Y.[Wei-Yue],
Zhang, X.[Xiao],
Shlens, J.[Jonathon],
Tan, M.X.[Ming-Xing],
Anguelov, D.[Dragomir],
PseudoAugment:
Learning to Use Unlabeled Data for Data Augmentation in Point Clouds,
ECCV22(XXXI:555-572).
Springer DOI
2211
BibRef
Huang, J.Q.[Jun-Qiang],
Kong, X.W.[Xiang-Wen],
Zhang, X.Y.[Xiang-Yu],
Revisiting the Critical Factors of Augmentation-Invariant
Representation Learning,
ECCV22(XXXI:42-58).
Springer DOI
2211
BibRef
Kalluri, T.[Tarun],
Sharma, A.[Astuti],
Chandraker, M.[Manmohan],
MemSAC: Memory Augmented Sample Consistency for Large Scale Domain
Adaptation,
ECCV22(XXX:550-568).
Springer DOI
2211
BibRef
Hwang, S.[Sehyun],
Lee, S.[Sohyun],
Kim, S.[Sungyeon],
Ok, J.[Jungseul],
Kwak, S.[Suha],
Combating Label Distribution Shift for Active Domain Adaptation,
ECCV22(XXXIII:549-566).
Springer DOI
2211
BibRef
Xu, Y.C.[Yue-Cong],
Yang, J.F.[Jian-Fei],
Cao, H.Z.[Hao-Zhi],
Wu, K.Y.[Ke-Yu],
Wu, M.[Min],
Chen, Z.H.[Zheng-Hua],
Source-Free Video Domain Adaptation by Learning Temporal Consistency
for Action Recognition,
ECCV22(XXXIV:147-164).
Springer DOI
2211
BibRef
Li, X.[Xinhao],
Li, J.J.[Jing-Jing],
Du, Z.[Zhekai],
Zhu, L.[Lei],
Li, W.[Wen],
Interpretable Open-Set Domain Adaptation via Angular Margin Separation,
ECCV22(XXXIV:1-18).
Springer DOI
2211
BibRef
Qu, S.Q.[San-Qing],
Chen, G.[Guang],
Zhang, J.[Jing],
Li, Z.J.[Zhi-Jun],
He, W.[Wei],
Tao, D.C.[Da-Cheng],
BMD: A General Class-Balanced Multicentric Dynamic Prototype Strategy
for Source-Free Domain Adaptation,
ECCV22(XXXIV:165-182).
Springer DOI
2211
BibRef
Lin, K.Y.[Kun-Yu],
Zhou, J.[JiaMing],
Qiu, Y.K.[Yu-Kun],
Zheng, W.S.[Wei-Shi],
Adversarial Partial Domain Adaptation by Cycle Inconsistency,
ECCV22(XXXIII:530-548).
Springer DOI
2211
BibRef
Roy, S.[Subhankar],
Trapp, M.[Martin],
Pilzer, A.[Andrea],
Kannala, J.H.[Ju-Ho],
Sebe, N.[Nicu],
Ricci, E.[Elisa],
Solin, A.[Arno],
Uncertainty-Guided Source-Free Domain Adaptation,
ECCV22(XXV:537-555).
Springer DOI
2211
BibRef
Kundu, J.N.[Jogendra Nath],
Bhambri, S.[Suvaansh],
Kulkarni, A.[Akshay],
Sarkar, H.[Hiran],
Jampani, V.[Varun],
Babu, R.V.[R. Venkatesh],
Concurrent Subsidiary Supervision for Unsupervised Source-Free Domain
Adaptation,
ECCV22(XXX:177-194).
Springer DOI
2211
BibRef
Hu, J.[Jian],
Zhong, H.[Haowen],
Yang, F.[Fei],
Gong, S.G.[Shao-Gang],
Wu, G.[Guile],
Yan, J.C.[Jun-Chi],
Learning Unbiased Transferability for Domain Adaptation by Uncertainty
Modeling,
ECCV22(XXXI:223-241).
Springer DOI
2211
BibRef
Liu, J.[Jie],
Guo, X.Q.[Xiao-Qing],
Yuan, Y.X.[Yi-Xuan],
Unknown-Oriented Learning for Open Set Domain Adaptation,
ECCV22(XXXIII:334-350).
Springer DOI
2211
BibRef
Ni, B.L.[Bo-Lin],
Peng, H.W.[Hou-Wen],
Chen, M.H.[Ming-Hao],
Zhang, S.Y.[Song-Yang],
Meng, G.F.[Gao-Feng],
Fu, J.L.[Jian-Long],
Xiang, S.M.[Shi-Ming],
Ling, H.B.[Hai-Bin],
Expanding Language-Image Pretrained Models for General Video
Recognition,
ECCV22(IV:1-18).
Springer DOI
2211
WWW Link.
BibRef
Kuchibhotla, H.C.[Hari Chandana],
Malagi, S.S.[Sumitra S.],
Chandhok, S.[Shivam],
Balasubramanian, V.N.[Vineeth N.],
Unseen Classes at a Later Time? No Problem,
CVPR22(9235-9244)
IEEE DOI
2210
Adaptation models, Protocols, Bidirectional control,
Benchmark testing, Pattern recognition, Unsupervised learning,
Self- semi- meta- unsupervised learning
BibRef
Li, S.T.[Shuang-Tong],
Zhou, T.Y.[Tian-Yi],
Tian, X.[Xinmei],
Tao, D.C.[Da-Cheng],
Learning to Collaborate in Decentralized Learning of Personalized
Models,
CVPR22(9756-9765)
IEEE DOI
2210
Training, Adaptation models, Costs, Network topology,
Computational modeling, Aggregates, Image edge detection,
Self- semi- meta- Machine learning
BibRef
Boudiaf, M.[Malik],
Mueller, R.[Romain],
Ben Ayed, I.[Ismail],
Bertinetto, L.[Luca],
Parameter-free Online Test-time Adaptation,
CVPR22(8334-8343)
IEEE DOI
2210
WWW Link. Training, Adaptation models, Maximum likelihood estimation,
Laplace equations, Uncertainty, Protocols, Memory management,
Transfer/low-shot/long-tail learning
BibRef
Yazdanpanah, M.[Moslem],
Moradi, P.[Parham],
Visual Domain Bridge: A source-free domain adaptation for
cross-domain few-shot learning,
FaDE-TCV22(2867-2876)
IEEE DOI
2210
Bridges, Training, Deep learning, Visualization, Neural networks
BibRef
Zhang, C.[Chi],
Cheng, Y.[Yalu],
Wei, P.X.[Peng-Xu],
He, H.L.[Hong-Liang],
Chen, J.[Jie],
CENet: Consolidation-and-Exploration Network for Continuous Domain
Adaptation,
RoSe22(3425-3431)
IEEE DOI
2210
Representation learning, Adaptation models,
Computational modeling, Reliability engineering, Data models
BibRef
Mirza, M.J.[M. Jehanzeb],
Micorek, J.[Jakub],
Possegger, H.[Horst],
Bischof, H.[Horst],
The Norm Must Go On: Dynamic Unsupervised Domain Adaptation by
Normalization,
CVPR22(14745-14755)
IEEE DOI
2210
Adaptation models, Statistical analysis, Computational modeling,
Training data, Performance gain,
Transfer/low-shot/long-tail learning
BibRef
Chen, L.[Liang],
Lou, Y.H.[Yi-Hang],
He, J.Z.[Jian-Zhong],
Bai, T.[Tao],
Deng, M.H.[Ming-Hua],
Geometric Anchor Correspondence Mining with Uncertainty Modeling for
Universal Domain Adaptation,
CVPR22(16113-16122)
IEEE DOI
2210
Representation learning, Manifolds, Adaptation models, Uncertainty,
Computational modeling, Logic gates, Representation learning,
Self- semi- meta- Transfer/low-shot/long-tail learning
BibRef
Xie, M.[Ming],
Li, Y.X.[Yu-Xi],
Wang, Y.[Yabiao],
Luo, Z.[Zekun],
Gan, Z.[Zhenye],
Sun, Z.Y.[Zhong-Yi],
Chi, M.[Mingmin],
Wang, C.J.[Cheng-Jie],
Wang, P.[Pei],
Learning Distinctive Margin toward Active Domain Adaptation,
CVPR22(7983-7992)
IEEE DOI
2210
Training, Support vector machines, Adaptation models,
Analytical models, Computational modeling, Scalability,
Self- semi- meta- unsupervised learning
BibRef
Liang, J.[Jian],
Hu, D.P.[Da-Peng],
Feng, J.S.[Jia-Shi],
He, R.[Ran],
DINE: Domain Adaptation from Single and Multiple Black-box Predictors,
CVPR22(7993-8003)
IEEE DOI
2210
Performance evaluation, Adaptation models, Smoothing methods,
Target recognition, Computational modeling, Predictive models,
Transfer/low-shot/long-tail learning
BibRef
Sun, T.[Tao],
Lu, C.[Cheng],
Zhang, T.S.[Tian-Shuo],
Ling, H.B.[Hai-Bin],
Safe Self-Refinement for Transformer-based Domain Adaptation,
CVPR22(7181-7190)
IEEE DOI
2210
Training, Adaptation models, Computational modeling,
Benchmark testing, Predictive models, Transformers, Data models,
Self- semi- meta- unsupervised learning
BibRef
Wang, Q.[Qin],
Fink, O.[Olga],
Van Gool, L.J.[Luc J.],
Dai, D.X.[Deng-Xin],
Continual Test-Time Domain Adaptation,
CVPR22(7191-7201)
IEEE DOI
2210
Adaptation models, Codes, Computational modeling, Neurons,
Data models, Entropy, Transfer/low-shot/long-tail learning,
Self- semi- meta- unsupervised learning
BibRef
Ding, N.[Ning],
Xu, Y.X.[Yi-Xing],
Tang, Y.[Yehui],
Xu, C.[Chao],
Wang, Y.H.[Yun-He],
Tao, D.C.[Da-Cheng],
Source-Free Domain Adaptation via Distribution Estimation,
CVPR22(7202-7212)
IEEE DOI
2210
Representation learning, Data privacy, Estimation, Training data,
Benchmark testing, Data models,
Self- semi- meta- unsupervised learning
BibRef
Shen, Y.[Yuefan],
Yang, Y.[Yanchao],
Yan, M.[Mi],
Wang, H.[He],
Zheng, Y.[Youyi],
Guibas, L.J.[Leonidas J.],
Domain Adaptation on Point Clouds via Geometry-Aware Implicits,
CVPR22(7213-7222)
IEEE DOI
2210
Point cloud compression, Training, Shape, Neural networks,
Robot sensing systems, Transfer/low-shot/long-tail learning,
Self- semi- meta- unsupervised learning
BibRef
Meng, R.[Rang],
Chen, W.J.[Wei-Jie],
Yang, S.[Shicai],
Song, J.[Jie],
Lin, L.[Luojun],
Xie, D.[Di],
Pu, S.L.[Shi-Liang],
Wang, X.C.[Xin-Chao],
Song, M.L.[Ming-Li],
Zhuang, Y.T.[Yue-Ting],
Slimmable Domain Adaptation,
CVPR22(7131-7140)
IEEE DOI
2210
Performance evaluation, Adaptation models, Visualization,
Uncertainty, Computational modeling, Stochastic processes,
Transfer/low-shot/long-tail learning
BibRef
Wang, F.[Fan],
Han, Z.Y.[Zhong-Yi],
Gong, Y.S.[Yong-Shun],
Yin, Y.L.[Yi-Long],
Exploring Domain-Invariant Parameters for Source Free Domain
Adaptation,
CVPR22(7141-7150)
IEEE DOI
2210
Adaptation models, Pattern recognition,
Classification algorithms, Image classification,
Machine learning
BibRef
Oliu, M.[Marc],
Bargal, S.A.[Sarah Adel],
Sclaroff, S.[Stan],
Baró, X.[Xavier],
Escalera, S.[Sergio],
Multi-varied Cumulative Alignment for Domain Adaptation,
CIAP22(II:324-334).
Springer DOI
2205
BibRef
Song, Y.[Yuru],
Lou, Z.[Zan],
You, S.[Shan],
Yang, E.K.[Er-Kun],
Wang, F.[Fei],
Qian, C.[Chen],
Zhang, C.S.[Chang-Shui],
Wang, X.G.[Xiao-Gang],
Learning with Privileged Tasks,
ICCV21(10665-10674)
IEEE DOI
2203
Training, Analytical models, Adaptation models, Correlation,
Computational modeling, Multitasking,
Recognition and classification
BibRef
Saito, K.[Kuniaki],
Saenko, K.[Kate],
OVANet: One-vs-All Network for Universal Domain Adaptation,
ICCV21(8980-8989)
IEEE DOI
2203
Entropy, Transfer/Low-shot/Semi/Unsupervised Learning,
Recognition and classification
BibRef
Ma, X.H.[Xin-Hong],
Gao, J.Y.[Jun-Yu],
Xu, C.S.[Chang-Sheng],
Active Universal Domain Adaptation,
ICCV21(8948-8957)
IEEE DOI
2203
Adaptation models, Uncertainty, Target recognition, Annotations,
Computational modeling, Semantics, Representation learning
BibRef
Prabhu, V.[Viraj],
Chandrasekaran, A.[Arjun],
Saenko, K.[Kate],
Hoffman, J.[Judy],
Active Domain Adaptation via Clustering Uncertainty-weighted
Embeddings,
ICCV21(8485-8494)
IEEE DOI
2203
Deep learning, Adaptation models, Uncertainty, Codes,
Neural networks, Labeling,
Representation learning
BibRef
Prabhu, V.[Viraj],
Khare, S.[Shivam],
Kartik, D.[Deeksha],
Hoffman, J.[Judy],
SENTRY: Selective Entropy Optimization via Committee Consistency for
Unsupervised Domain Adaptation,
ICCV21(8538-8547)
IEEE DOI
2203
Codes, Transforms, Benchmark testing, Prediction algorithms,
Approximation algorithms, Entropy,
Representation learning
BibRef
Awais, M.[Muhammad],
Zhou, F.W.[Feng-Wei],
Xu, H.[Hang],
Hong, L.Q.[Lan-Qing],
Luo, P.[Ping],
Bae, S.H.[Sung-Ho],
Li, Z.G.[Zhen-Guo],
Adversarial Robustness for Unsupervised Domain Adaptation,
ICCV21(8548-8557)
IEEE DOI
2203
Training, Adaptation models, Computational modeling,
Benchmark testing, Robustness,
Representation learning
BibRef
Li, K.[Kai],
Liu, C.[Chang],
Zhao, H.[Handong],
Zhang, Y.[Yulun],
Fu, Y.[Yun],
ECACL: A Holistic Framework for Semi-Supervised Domain Adaptation,
ICCV21(8558-8567)
IEEE DOI
2203
Adaptation models, Codes, Perturbation methods,
Computational modeling, Data models,
BibRef
Yue, Z.Q.[Zhong-Qi],
Sun, Q.[Qianru],
Hua, X.S.[Xian-Sheng],
Zhang, H.[Hanwang],
Transporting Causal Mechanisms for Unsupervised Domain Adaptation,
ICCV21(8579-8588)
IEEE DOI
2203
Adaptation models, Codes, Semantics, Benchmark testing,
Transfer/Low-shot/Semi/Unsupervised Learning, Recognition and classification
BibRef
Peng, J.H.[Jun-Han],
Su, J.[Jia],
Sun, Y.Q.[Yong-Qing],
Wang, Z.[Zheng],
Lin, C.W.[Chia-Wen],
Semantic Nighttime Image Segmentation Via Illumination and Position
Aware Domain Adaptation,
ICIP21(1034-1038)
IEEE DOI
2201
Image segmentation, Adaptation models, Semantics, Neural networks,
Lighting, Task analysis, Nighttime Semantic Segmentation,
Self-attention
BibRef
Wang, Q.[Qian],
Breckon, T.P.[Toby P.],
Source Class Selection With Label Propagation for Partial Domain
Adaptation,
ICIP21(769-773)
IEEE DOI
2201
Handheld computers, Image processing, Benchmark testing,
Task analysis, Convergence, Partial Domain Adaptation,
Locality Preserving Projection
BibRef
Feng, C.[Cheng],
Zhong, C.L.[Chao-Liang],
Wang, J.[Jie],
Sun, J.[Jun],
Yokota, Y.[Yasuto],
EBB: Progressive Optimization for Partial Domain Adaptation,
ICIP21(734-738)
IEEE DOI
2201
Adaptation models, Handheld computers, Image processing,
Transfer learning, Optimization methods, Resists,
Progressive optimization
BibRef
Yang, L.[Li],
He, Z.[Zhezhi],
Zhang, J.[Junshan],
Fan, D.L.[De-Liang],
KSM: Fast Multiple Task Adaption via Kernel-wise Soft Mask Learning,
CVPR21(13840-13848)
IEEE DOI
2111
Training, Learning systems, Knowledge engineering,
Adaptation models, Costs, Tensors, Computational modeling
BibRef
Tomani, C.[Christian],
Gruber, S.[Sebastian],
Erdem, M.E.[Muhammed Ebrar],
Cremers, D.[Daniel],
Buettner, F.[Florian],
Post-hoc Uncertainty Calibration for Domain Drift Scenarios,
CVPR21(10119-10127)
IEEE DOI
2111
Deep learning, Uncertainty, Perturbation methods,
Computational modeling, Calibration
BibRef
Huang, J.W.[Jing-Wei],
Huang, S.[Shan],
Sun, M.W.[Ming-Wei],
DeepLM: Large-scale Nonlinear Least Squares on Deep Learning
Frameworks using Stochastic Domain Decomposition,
CVPR21(10303-10312)
IEEE DOI
2111
Deep learning, Jacobian matrices, Bundle adjustment,
Codes, Memory management, Pattern recognition
BibRef
Luo, Y.W.[You-Wei],
Ren, C.X.[Chuan-Xian],
Conditional Bures Metric for Domain Adaptation,
CVPR21(13984-13993)
IEEE DOI
2111
Measurement, Analytical models, Adaptation models,
Computational modeling, Estimation, Pattern recognition
BibRef
Liang, J.[Jian],
Hu, D.P.[Da-Peng],
Feng, J.S.[Jia-Shi],
Domain Adaptation with Auxiliary Target Domain-Oriented Classifier,
CVPR21(16627-16637)
IEEE DOI
2111
Handheld computers, Semantics, Focusing,
Semisupervised learning, Benchmark testing, Pattern recognition
BibRef
Savarese, P.[Pedro],
McAllester, D.[David],
Babu, S.[Sudarshan],
Maire, M.[Michael],
Domain-Independent Dominance of Adaptive Methods,
CVPR21(16281-16290)
IEEE DOI
2111
Training, Couplings, Stochastic processes, Tools,
Pattern recognition, Task analysis
BibRef
Lee, S.[Seunghun],
Cho, S.[Sunghyun],
Im, S.H.[Sung-Hoon],
DRANet: Disentangling Representation and Adaptation Networks for
Unsupervised Cross-Domain Adaptation,
CVPR21(15247-15256)
IEEE DOI
2111
Training, Adaptation models, Image segmentation, Visualization,
Semantics, Network architecture, Image representation
BibRef
Li, S.[Shuang],
Xie, M.[Mixue],
Gong, K.X.[Kai-Xiong],
Liu, C.H.[Chi Harold],
Wang, Y.L.[Yu-Lin],
Li, W.[Wei],
Transferable Semantic Augmentation for Domain Adaptation,
CVPR21(11511-11520)
IEEE DOI
2111
Upper bound, Target recognition, Semantics,
Gaussian distribution, Benchmark testing, Pattern recognition
BibRef
Li, S.[Shuang],
Zhang, J.[JinMing],
Ma, W.X.[Wen-Xuan],
Liu, C.H.[Chi Harold],
Li, W.[Wei],
Dynamic Domain Adaptation for Efficient Inference,
CVPR21(7828-7837)
IEEE DOI
2111
Training, Deep learning, Computational modeling,
Benchmark testing, Predictive models, Real-time systems
BibRef
Fu, B.[Bo],
Cao, Z.J.[Zhang-Jie],
Wang, J.[Jianmin],
Long, M.S.[Ming-Sheng],
Transferable Query Selection for Active Domain Adaptation,
CVPR21(7268-7277)
IEEE DOI
2111
Training, Uncertainty, Costs, Annotations,
Supervised learning, Benchmark testing
BibRef
Zhang, W.C.[Wei-Chen],
Li, W.[Wen],
Xu, D.[Dong],
SRDAN: Scale-aware and Range-aware Domain Adaptation Network for
Cross-dataset 3D Object Detection,
CVPR21(6765-6775)
IEEE DOI
2111
Semantics,
Object detection, Feature extraction, Pattern recognition, Task analysis
BibRef
Shi, Z.[Zheng],
Tseng, E.[Ethan],
Bijelic, M.[Mario],
Ritter, W.[Werner],
Heide, F.[Felix],
ZeroScatter: Domain Transfer for Long Distance Imaging and Vision
through Scattering Media,
CVPR21(3475-3485)
IEEE DOI
2111
Training, Rain, Snow, Scattering, Training data, Object detection
BibRef
Yu, Q.[Qing],
Hashimoto, A.[Atsushi],
Ushiku, Y.[Yoshitaka],
Divergence Optimization for Noisy Universal Domain Adaptation,
CVPR21(2515-2524)
IEEE DOI
2111
Adaptation models, Annotations,
Computational modeling, Benchmark testing, Generators, Pattern recognition
BibRef
Li, J.[Jichang],
Li, G.[Guanbin],
Shi, Y.[Yemin],
Yu, Y.Z.[Yi-Zhou],
Cross-Domain Adaptive Clustering for Semi-Supervised Domain
Adaptation,
CVPR21(2505-2514)
IEEE DOI
2111
Training, Adaptation models, Training data,
Benchmark testing, Data models, Adversarial machine learning
BibRef
Li, B.[Bo],
Wang, Y.Z.[Ye-Zhen],
Zhang, S.H.[Shang-Hang],
Li, D.S.[Dong-Sheng],
Keutzer, K.[Kurt],
Darrell, T.J.[Trevor J.],
Zhao, H.[Han],
Learning Invariant Representations and Risks for Semi-supervised
Domain Adaptation,
CVPR21(1104-1113)
IEEE DOI
2111
Training, Shape, Snow, Supervised learning, Fasteners,
Minimization, Classification algorithms
BibRef
Thota, M.[Mamatha],
Leontidis, G.[Georgios],
Contrastive Domain Adaptation,
WiCV21(2209-2218)
IEEE DOI
2109
Training, Adaptation models, Visualization,
Machine learning algorithms, Pipelines, Machine learning
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
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.L.[Jiao-Long],
Zhao, D.W.[Da-Wei],
Wang, Z.Y.[Zhi-Yu],
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
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
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
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],
Wang, Y.[Yan],
Gao, M.[Mingfei],
Shrivastava, A.[Abhinav],
Weinberger, K.Q.[Kilian Q.],
Chao, W.L.[Wei-Lun],
Lim, S.N.[Ser-Nam],
Deep Co-Training with Task Decomposition for Semi-Supervised Domain
Adaptation,
ICCV21(8886-8896)
IEEE DOI
2203
Training, Adaptation models, Codes, Art, Semisupervised learning,
Data models, Transfer/Low-shot/Semi/Unsupervised Learning,
Recognition and classification
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
Menapace, W.[Willi],
Lathuilière, S.[Stéphane],
Ricci, E.[Elisa],
Learning to Cluster Under Domain Shift,
ECCV20(XXVIII:736-752).
Springer DOI
2011
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
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
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
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
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.C.[Kai-Chao],
Long, M.S.[Ming-Sheng],
Cao, Z.J.[Zhang-Jie],
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
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
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
Yang, S.Q.[Shi-Qi],
Wang, Y.X.[Ya-Xing],
van de Weijer, J.[Joost],
Herranz, L.[Luis],
Jui, S.L.[Shang-Ling],
Generalized Source-free Domain Adaptation,
ICCV21(8958-8967)
IEEE DOI
2203
Training, Adaptation models, Codes, Data models,
Transfer/Low-shot/Semi/Unsupervised Learning, Recognition and classification
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
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.Q.[Si-Qi],
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
Multi-Source Domain Adaptation .