14.1.6 Domain Adaptation

Chapter Contents (Back)
Domain Adaption. A lot of similarity to Transfer Learning:
See also Transfer Learning from Other Classes.
See also Unsupervised Domain Adaptation.
See also Knowledge Distillation.

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) BibRef

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

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 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 Cross-Domain Image Manipulation and Classification,
IP(29), 2020, pp. 2795-2807.
IEEE DOI 2001
Representation disentanglement, image translation, domain adaptation, deep learning BibRef

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

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

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

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

Zhang, Z.[Zhen], Wang, M.Z.[Mian-Zhi], Nehorai, A.[Arye],
Optimal Transport in Reproducing Kernel Hilbert Spaces: Theory and Applications,
PAMI(42), No. 7, July 2020, pp. 1741-1754.
IEEE DOI 2006
Omparing and matching distributions in reproducing kernel Hilbert spaces. Kernel, Covariance matrices, Hilbert space, Task analysis, Geometry, Modeling, Optimal transport, reproducing kernel hilbert spaces, domain adaptation BibRef

Zhang, Z.[Zhen], Wang, M.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], Einalou, Z.[Zahra],
Logistic regression projection-based feature representation for visual domain adaptation,
SIViP(14), No. 6, September 2020, pp. 1115-1123.
WWW Link. 2008
BibRef

Karimpour, M.[Morvarid], Saray, S.N.[Shiva Noori], Tahmoresnezhad, J.[Jafar], Aghababa, M.P.[Mohammad Pourmahmood],
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 transformations,
MVA(31), No. 6, August 2020, pp. Article42.
Springer DOI 2008
BibRef

Mancini, M.[Massimiliano], Akata, Z.[Zeynep], Ricci, E.[Elisa], Caputo, B.[Barbara],
Towards Recognizing Unseen Categories in Unseen Domains,
ECCV20(XXIII:466-483).
Springer DOI 2011
BibRef

Chen, S., Harandi, M., 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 Alignment for Remote Sensing Image Scene Classification,
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, attention 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) BibRef

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], Celebi, M.E.[M. Emre],
Advances in domain adaptation for computer vision,
IVC(114), 2021, pp. 104268.
Elsevier DOI 2109
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

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 .


Last update:Sep 12, 2021 at 22:38:33