14.1.7 Domain Adaptation

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

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

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 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

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

Roy, S.[Subhankar], Krivosheev, E.[Evgeny], Zhong, Z.[Zhun], Sebe, N.[Nicu], Ricci, E.[Elisa],
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 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

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.X.[Yu-Xi], 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

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.M.[Fu-Ming], 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

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

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. 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, Data models, Training, remote sensing 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

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 BibRef

Wang, Q.[Qian], Breckon, T.P.[Toby P.],
Cross-domain structure preserving projection for heterogeneous domain adaptation,
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 BibRef

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.Y.[Zi-Yan],
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.Y.[Zi-Yan], 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

Wang, H.X.[Hai-Xin], Sun, J.[Jinan], Luo, X.[Xiao], Xiang, W.[Wei], Zhang, S.K.[Shi-Kun], Chen, C.[Chong], Hua, X.S.[Xian-Sheng],
Toward Effective Domain Adaptive Retrieval,
IP(32), 2023, pp. 1285-1299.
IEEE DOI 2303
Semantics, Optimization, Task analysis, Uncertainty, Noise measurement, Measurement uncertainty, Benchmark testing, domain adaptive retrieval BibRef

Huang, Z.[Zenan], Wen, J.[Jun], Chen, S.[Siheng], Zhu, L.C.[Lin-Chao], Zheng, N.[Nenggan],
Discriminative Radial Domain Adaptation,
IP(32), 2023, pp. 1419-1431.
IEEE DOI 2303
Feature extraction, Training, Shape, Adaptation models, Task analysis, Computer science, Bridges, Domain adaptation, radial structure matching BibRef

Zhang, Y.X.[Yu-Xiang], Li, W.[Wei], Sun, W.D.[Wei-Dong], Tao, R.[Ran], Du, Q.[Qian],
Single-Source Domain Expansion Network for Cross-Scene Hyperspectral Image Classification,
IP(32), 2023, pp. 1498-1512.
IEEE DOI 2303
Training, Generators, Feature extraction, Task analysis, Semantics, Image classification, Hyperspectral imaging, contrastive learning BibRef

Wang, H.Y.[Hao-Yu], Cheng, Y.[Yuhu], Wang, X.S.[Xue-Song],
A Novel Hyperspectral Image Classification Method Using Class-Weighted Domain Adaptation Network,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link 2303
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Xu, Y.B.[Yan-Bing], Zhang, Y.M.[Yan-Mei], Yue, T.X.[Ting-Xuan], Yu, C.C.[Cheng-Cheng], Li, H.[Huan],
Graph-Based Domain Adaptation Few-Shot Learning for Hyperspectral Image Classification,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link 2303
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Huang, Z.Y.[Zhi-Yong], Sheng, K.[Kekai], Li, K.[Ke], Liang, J.[Jian], Yao, T.P.[Tai-Ping], Dong, W.M.[Wei-Ming], Zhou, D.W.[Deng-Wen], Sun, X.[Xing],
Reciprocal normalization for domain adaptation,
PR(140), 2023, pp. 109533.
Elsevier DOI 2305
Domain adaptation, Feature normalization, Deep neural network BibRef

Liu, X.Y.[Xiao-Yong], Dong, Z.Y.[Zi-Yang], Li, H.H.[Hui-Hui], Ren, J.C.[Jin-Chang], Zhao, H.M.[Hui-Min], Li, H.[Hao], Chen, W.Q.[Wei-Qi], Xiao, Z.[Zhanhao],
H-RNet: Hybrid Relation Network for Few-Shot Learning-Based Hyperspectral Image Classification,
RS(15), No. 10, 2023, pp. xx-yy.
DOI Link 2306
BibRef

Li, P.F.[Peng-Fang], Liu, F.[Fang], Jiao, L.C.[Li-Cheng], Li, S.[Shuo], Li, L.L.[Ling-Ling], Liu, X.[Xu], Huang, X.[Xinyan],
Knowledge transduction for cross-domain few-shot learning,
PR(141), 2023, pp. 109652.
Elsevier DOI 2306
Few-shot learning, Domain adaptation, Feature adaptation, Feature transduction, Feed-forward attention, Deep sparse representation BibRef

Li, W.[Weikai], Chen, S.C.[Song-Can],
Partial Domain Adaptation Without Domain Alignment,
PAMI(45), No. 7, July 2023, pp. 8787-8797.
IEEE DOI 2306
Adaptation models, Manifolds, Knowledge transfer, Benchmark testing, Training, Task analysis, Standards, manifold learning BibRef

Su, W.[Wan], Han, Z.Y.[Zhong-Yi], He, R.[Rundong], Wei, B.[Benzheng], He, X.Y.[Xue-Ying], Yin, Y.L.[Yi-Long],
Neighborhood-based credibility anchor learning for universal domain adaptation,
PR(142), 2023, pp. 109686.
Elsevier DOI 2307
Universal domain adaptation, Threshold-free, Neighborhood learning BibRef

Wang, W.[Wei], Wang, M.Z.[Meng-Zhu], Dong, X.[Xiao], Lan, L.[Long], Zu, Q.N.[Quan-Nan], Zhang, X.[Xiang], Wang, C.[Cong],
Class-specific and self-learning local manifold structure for domain adaptation,
PR(142), 2023, pp. 109654.
Elsevier DOI 2307
Domain adaptation, Wrongly labeled, Feature-corrupted, Local manifold, Global discriminative, Self-learning BibRef

Siry, R.[Rodrigue], Hémadou, L.[Louis], Simon, L.[Loïc], Jurie, F.[Frédéric],
On the inductive biases of deep domain adaptation,
CVIU(233), 2023, pp. 103714.
Elsevier DOI 2307
Domain adaptation, Machine Learning, Deep Learning BibRef

Yu, Z.[Zhiqi], Li, J.J.[Jing-Jing], Zhu, L.[Lei], Lu, K.[Ke], Shen, H.T.[Heng Tao],
Uneven Bi-Classifier Learning for Domain Adaptation,
CirSysVideo(33), No. 7, July 2023, pp. 3398-3408.
IEEE DOI 2307
Feature extraction, Training, Data mining, Measurement, Adaptation models, Optimization, Loss measurement, adversarial learning BibRef

Jing, M.M.[Meng-Meng], Meng, L.C.[Li-Chao], Li, J.J.[Jing-Jing], Zhu, L.[Lei], Shen, H.T.[Heng Tao],
Adversarial Mixup Ratio Confusion for Unsupervised Domain Adaptation,
MultMed(25), 2023, pp. 2559-2572.
IEEE DOI 2307
Generators, Training, Adversarial machine learning, Deep learning, Adaptation models, Neural networks, Knowledge transfer, transfer learning BibRef

Yang, S.Q.[Shi-Qi], Wang, Y.X.[Ya-Xing], Herranz, L.[Luis], Jui, S.L.[Shang-Ling], van de Weijer, J.[Joost],
Casting a BAIT for offline and online source-free domain adaptation,
CVIU(234), 2023, pp. 103747.
Elsevier DOI 2307
BibRef
Earlier: A1, A2, A5, A3, A4:
Generalized Source-free Domain Adaptation,
ICCV21(8958-8967)
IEEE DOI 2203
Source-free domain adaptation, Online domain adaptation. 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

Koh, K.B.[Kian Boon], Fernando, B.[Basura],
Consistency Regularization for Domain Adaptation,
OutDistri22(347-359).
Springer DOI 2304
BibRef

Rahman, M.M.[Md Mahmudur], Panda, R.[Rameswar], Ul Alam, M.A.[Mohammad Arif],
Semi-Supervised Domain Adaptation with Auto-Encoder via Simultaneous Learning,
WACV23(402-411)
IEEE DOI 2302
Training, Adaptation models, Computational modeling, Linear programming, Convergence, Vision + language and/or other modalities BibRef

Kothandaraman, D.[Divya], Shekhar, S.[Sumit], Sancheti, A.[Abhilasha], Ghuhan, M.[Manoj], Shukla, T.[Tripti], Manocha, D.[Dinesh],
SALAD: Source-free Active Label-Agnostic Domain Adaptation for Classification, Segmentation and Detection,
WACV23(382-391)
IEEE DOI 2302
Knowledge engineering, Visualization, Image segmentation, Data privacy, Uncertainty, Codes, algorithms, (including transfer) BibRef

Sahoo, A.[Aadarsh], Panda, R.[Rameswar], Feris, R.[Rogerio], Saenko, K.[Kate], Das, A.[Abir],
Select, Label, and Mix: Learning Discriminative Invariant Feature Representations for Partial Domain Adaptation,
WACV23(4199-4208)
IEEE DOI 2302
Benchmark testing, Algorithms: Machine learning architectures, formulations, and algorithms (including transfer) BibRef

Cardace, A.[Adriano], Spezialetti, R.[Riccardo], Ramirez, P.Z.[Pierluigi Zama], Salti, S.[Samuele], di Stefano, L.[Luigi],
Self-Distillation for Unsupervised 3D Domain Adaptation,
WACV23(4155-4166)
IEEE DOI 2302
Point cloud compression, Training, Art, Benchmark testing, Graph neural networks, Algorithms: 3D computer vision. BibRef

Chhabra, S.[Sachin], Venkateswara, H.[Hemanth], Li, B.X.[Bao-Xin],
Generative Alignment of Posterior Probabilities for Source-free Domain Adaptation,
WACV23(4114-4123)
IEEE DOI 2302
Adaptation models, MIMICs, Generators, Algorithms: Machine learning architectures, formulations, algorithms (including transfer) BibRef

Hur, S.[Sungsu], Shin, I.[Inkyu], Park, K.Y.[Kwan-Yong], Woo, S.[Sanghyun], Kweon, I.S.[In So],
Learning Classifiers of Prototypes and Reciprocal Points for Universal Domain Adaptation,
WACV23(531-540)
IEEE DOI 2302
Training, Adaptation models, Prototypes, Benchmark testing, Minimization, Algorithms: Machine learning architectures, and algorithms (including transfer) BibRef

Essich, M.[Michael], Rehmann, M.[Markus], Curio, C.[Cristóbal],
Auxiliary Task-Guided CycleGAN for Black-Box Model Domain Adaptation,
WACV23(541-550)
IEEE DOI 2302
Training, Adaptation models, Pose estimation, Closed box, Benchmark testing, Multitasking, and algorithms (including transfer) BibRef

Chen, H.X.[Hao-Xing], Li, H.X.[Hua-Xiong], Li, Y.H.[Yao-Hui], Chen, C.L.[Chun-Lin],
Multi-Scale Adaptive Task Attention Network for Few-Shot Learning,
ICPR22(4765-4771)
IEEE DOI 2212
Adaptive systems, Source coding, Benchmark testing, Feature extraction, Generators, Task analysis 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.Y.[Shu-Yang], 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
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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.M.[Jia-Ming], 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.F.[Yue-Fan], Yang, Y.C.[Yan-Chao], 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.M.[Jian-Min], 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.C.[Ji-Chang], Li, G.B.[Guan-Bin], 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
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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.M.[Jian-Min], 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

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 .