Gong, B.Q.[Bo-Qing],
Grauman, K.[Kristen],
Sha, F.[Fei],
Learning Kernels for Unsupervised Domain Adaptation with Applications
to Visual Object Recognition,
IJCV(109), No. 1-2, August 2014, pp. 3-27.
Springer DOI
1407
Correct mismatch between source and target domain.
BibRef
Gong, B.Q.[Bo-Qing],
Shi, Y.[Yuan],
Sha, F.[Fei],
Grauman, K.[Kristen],
Geodesic flow kernel for unsupervised domain adaptation,
CVPR12(2066-2073).
IEEE DOI
1208
BibRef
Gopalan, R.[Raghuraman],
Li, R.N.[Ruo-Nan],
Chellappa, R.[Rama],
Unsupervised Adaptation Across Domain Shifts by Generating
Intermediate Data Representations,
PAMI(36), No. 11, November 2014, pp. 2288-2302.
IEEE DOI
1410
BibRef
Earlier:
Domain adaptation for object recognition: An unsupervised approach,
ICCV11(999-1006).
IEEE DOI
1201
Adaptation models.
Adapting based on training on different domain.
BibRef
Li, R.N.[Ruo-Nan],
Patel, V.M.[Vishal M.],
Gopalan, R.[Raghuraman],
Chellappa, R.[Rama],
Domain Adaptation for Visual Recognition,
FTCGV(8), No. 4, 2015, pp. 285-378.
DOI Link
1503
BibRef
Patel, V.M.[Vishal M.],
Gopalan, R.[Raghuraman],
Li, R.N.[Ruo-Nan],
Chellappa, R.,
Visual Domain Adaptation: A survey of recent advances,
SPMag(32), No. 3, May 2015, pp. 53-69.
IEEE DOI
1504
Classification algorithms
BibRef
Samanta, S.,
Das, S.,
Unsupervised domain adaptation using eigenanalysis in kernel space
for categorisation tasks,
IET-IPR(9), No. 11, 2015, pp. 925-930.
DOI Link
1511
Hilbert spaces
BibRef
Selvan, A.T.,
Samanta, S.,
Das, S.,
Domain adaptation using weighted sub-space sampling for object
categorization,
ICAPR15(1-5)
IEEE DOI
1511
differential geometry
BibRef
Fernando, B.[Basura],
Tommasi, T.[Tatiana],
Tuytelaars, T.[Tinne],
Joint cross-domain classification and subspace learning for
unsupervised adaptation,
PRL(65), No. 1, 2015, pp. 60-66.
Elsevier DOI
1511
BibRef
Earlier: A2, A3, Only:
A Testbed for Cross-Dataset Analysis,
TASKCV14(18-31).
Springer DOI
1504
Unsupervised domain adaptation
BibRef
Redko, I.[Ievgen],
Bennani, Y.[Younès],
Non-negative embedding for fully unsupervised domain adaptation,
PRL(77), No. 1, 2016, pp. 35-41.
Elsevier DOI
1606
BibRef
And:
Kernel alignment for unsupervised transfer learning,
ICPR16(525-530)
IEEE DOI
1705
Bridges, Indexes, Kernel, Optimization, Partitioning algorithms,
Standards, Symmetric matrices.
Transfer learning
BibRef
Zhu, L.[Lei],
Ma, L.[Li],
Class centroid alignment based domain adaptation for classification
of remote sensing images,
PRL(83, Part 2), No. 1, 2016, pp. 124-132.
Elsevier DOI
1609
Domain adaptation
BibRef
Ma, L.[Li],
Crawford, M.M.[Melba M.],
Zhu, L.[Lei],
Liu, Y.[Yong],
Centroid and Covariance Alignment-Based Domain Adaptation for
Unsupervised Classification of Remote Sensing Images,
GeoRS(57), No. 4, April 2019, pp. 2305-2323.
IEEE DOI
1904
geophysical image processing, image classification,
image filtering, remote sensing, spatial filters,
remote sensing
BibRef
Liu, Z.X.[Zi-Xu],
Ma, L.[Li],
Du, Q.[Qian],
Class-Wise Distribution Adaptation for Unsupervised Classification of
Hyperspectral Remote Sensing Images,
GeoRS(59), No. 1, January 2021, pp. 508-521.
IEEE DOI
2012
Feature extraction, Hyperspectral imaging, Neural networks,
Generative adversarial networks,
remote sensing
BibRef
Hou, C.A.[Cheng-An],
Tsai, Y.H.H.[Yao-Hung Hubert],
Yeh, Y.R.[Yi-Ren],
Wang, Y.C.A.F.[Yu-Chi-Ang Frank],
Unsupervised Domain Adaptation With Label and Structural Consistency,
IP(25), No. 12, December 2016, pp. 5552-5562.
IEEE DOI
1612
BibRef
Earlier: A2, A3, A4, Only:
Learning Cross-Domain Landmarks for Heterogeneous Domain Adaptation,
CVPR16(5081-5090)
IEEE DOI
1612
pattern classification
BibRef
Chen, W.Y.[Wei-Yu],
Hsu, T.M.H.[Tzu-Ming Harry],
Tsai, Y.H.H.[Yao-Hung Hubert],
Wang, Y.C.A.F.[Yu-Chi-Ang Frank],
Chen, M.S.[Ming-Syan],
Transfer Neural Trees for Heterogeneous Domain Adaptation,
ECCV16(V: 399-414).
Springer DOI
1611
BibRef
Hsu, T.M.H.[Tzu-Ming Harry],
Chen, W.Y.[Wei-Yu],
Hou, C.A.[Cheng-An],
Tsai, Y.H.H.,
Yeh, Y.R.[Yi-Ren],
Wang, Y.C.A.F.[Yu-Chi-Ang Frank],
Unsupervised Domain Adaptation with Imbalanced Cross-Domain Data,
ICCV15(4121-4129)
IEEE DOI
1602
Computer vision
BibRef
Chen, W.Y.[Wei-Yu],
Hsu, T.M.H.[Tzu-Ming Harry],
Hou, C.A.[Cheng-An],
Yeh, Y.R.[Yi-Ren],
Wang, Y.C.A.F.[Yu-Chi-Ang Frank],
Connecting the dots without clues: Unsupervised domain adaptation for
cross-domain visual classification,
ICIP15(3997-4001)
IEEE DOI
1512
BibRef
Earlier: A3, A4, A5, Only:
An unsupervised domain adaptation approach for cross-domain visual
classification,
AVSS15(1-6)
IEEE DOI
1511
Unsupervised domain adaptation
motion estimation
BibRef
Chou, Y.C.[Yen-Cheng],
Wei, C.P.[Chia-Po],
Wang, Y.C.A.F.[Yu-Chi-Ang Frank],
A discriminative domain adaptation model for cross-domain image
classification,
ICIP13(3083-3087)
IEEE DOI
1402
Domain adaptation; image classification; low-rank matrix decomposition
BibRef
Li, C.G.[Chun-Guang],
You, C.,
Vidal, R.[Rene],
Structured Sparse Subspace Clustering: A Joint Affinity Learning and
Subspace Clustering Framework,
IP(26), No. 6, June 2017, pp. 2988-3001.
IEEE DOI
1705
BibRef
Earlier: A1, A3, Only:
Structured Sparse Subspace Clustering:
A unified optimization framework,
CVPR15(277-286)
IEEE DOI
1510
Cancer, Face, Motion segmentation, Optimization,
Sparse matrices, Videos, Structured sparse subspace clustering,
cancer clustering, constrained subspace clustering,
structured subspace clustering, subspace, structured, norm
BibRef
Patel, V.M.[Vishal M.],
Vidal, R.[Rene],
Kernel sparse subspace clustering,
ICIP14(2849-2853)
IEEE DOI
1502
Clustering algorithms
BibRef
Shrivastava, A.[Ashish],
Shekhar, S.[Sumit],
Patel, V.M.[Vishal M.],
Unsupervised domain adaptation using parallel transport on Grassmann
manifold,
WACV14(277-284)
IEEE DOI
1406
Clustering algorithms
BibRef
Nayak, G.K.[Gaurav Kumar],
Mopuri, K.R.[Konda Reddy],
Jain, S.[Saksham],
Chakraborty, A.[Anirban],
Mining Data Impressions From Deep Models as Substitute for the
Unavailable Training Data,
PAMI(44), No. 11, November 2022, pp. 8465-8481.
IEEE DOI
2210
Data models, Adaptation models, Training data, Data mining, Training,
Task analysis, Computational modeling, Data impressions,
unsupervised domain adaptation
BibRef
Ranganathan, H.,
Venkateswara, H.[Hemanth],
Chakraborty, S.[Shayok],
Panchanathan, S.[Sethuraman],
Deep active learning for image classification,
ICIP17(3934-3938)
IEEE DOI
1803
Computational modeling, Entropy, Labeling,
Machine learning, Training, Uncertainty,
entropy
BibRef
Venkateswara, H.[Hemanth],
Eusebio, J.,
Chakraborty, S.[Shayok],
Panchanathan, S.[Sethuraman],
Deep Hashing Network for Unsupervised Domain Adaptation,
CVPR17(5385-5394)
IEEE DOI
1711
Adaptation models, Data models, Machine learning,
Neural networks, Training
BibRef
Paris, C.,
Bruzzone, L.,
A Novel Approach to the Unsupervised Extraction of Reliable Training
Samples From Thematic Products,
GeoRS(59), No. 3, March 2021, pp. 1930-1948.
IEEE DOI
2103
Training, Reliability, Databases, Semantics, Data mining,
Spatial resolution, Remote sensing, Land-cover map update,
weak learning classification
BibRef
Lu, H.,
Shen, C.,
Cao, Z.,
Xiao, Y.,
van den Hengel, A.,
An Embarrassingly Simple Approach to Visual Domain Adaptation,
IP(27), No. 7, July 2018, pp. 3403-3417.
IEEE DOI
1805
Adaptation models, Closed-form solutions, Iterative methods,
Robustness, Training, Training data, Visualization,
scene classification
BibRef
Lu, H.,
Zhang, L.,
Cao, Z.,
Wei, W.,
Xian, K.,
Shen, C.,
van den Hengel, A.,
When Unsupervised Domain Adaptation Meets Tensor Representations,
ICCV17(599-608)
IEEE DOI
1802
image representation,
learning (artificial intelligence), matrix algebra, tensors,
Visualization
BibRef
Yang, B.Y.[Bao-Yao],
Ma, A.J.[Andy J.],
Yuen, P.C.[Pong C.],
Learning domain-shared group-sparse representation for unsupervised
domain adaptation,
PR(81), 2018, pp. 615-632.
Elsevier DOI
1806
Domain adaptation, Dictionary learning
BibRef
Yang, B.Y.[Bao-Yao],
Yuen, P.C.[Pong C.],
Learning adaptive geometry for unsupervised domain adaptation,
PR(110), 2021, pp. 107638.
Elsevier DOI
2011
Domain adaptation, Manifold structure, Distribution alignment
BibRef
Chen, Y.[Yu],
Yang, C.L.[Chun-Ling],
Zhang, Y.[Yan],
Deep domain similarity Adaptation Networks for across domain
classification,
PRL(112), 2018, pp. 270-276.
Elsevier DOI
1809
Deep learning, Domain adaptation, Domain similarity, Image classification
BibRef
Chen, Y.[Yu],
Yang, C.L.[Chun-Ling],
Zhang, Y.[Yan],
Li, Y.[Yuze],
Deep conditional adaptation networks and label correlation transfer
for unsupervised domain adaptation,
PR(98), 2020, pp. 107072.
Elsevier DOI
1911
Conditional domain adaptation, Deep learning,
Unsupervised learning, Label transfer
BibRef
Huang, J.[Junchu],
Zhou, Z.H.[Zhi-Heng],
Transfer metric learning for unsupervised domain adaptation,
IET-IPR(13), No. 5, 18 April 2019, pp. 804-810.
DOI Link
1904
BibRef
Zhang, L.,
Wang, P.,
Wei, W.,
Lu, H.,
Shen, C.,
van den Hengel, A.,
Zhang, Y.,
Unsupervised Domain Adaptation Using Robust Class-Wise Matching,
CirSysVideo(29), No. 5, May 2019, pp. 1339-1349.
IEEE DOI
1905
Robustness, Image color analysis, Visualization, Data models,
Computer science, Australia, Adaptation models,
unsupervised domain adaptation
BibRef
Le, T.N.[Tien-Nam],
Habrard, A.[Amaury],
Sebban, M.[Marc],
Deep multi-Wasserstein unsupervised domain adaptation,
PRL(125), 2019, pp. 249-255.
Elsevier DOI
1909
Domain adaptation, Deep learning, Wasserstein metric, Optimal transport
BibRef
Liang, J.[Jian],
He, R.[Ran],
Sun, Z.A.[Zhen-An],
Tan, T.N.[Tie-Niu],
Exploring uncertainty in pseudo-label guided unsupervised domain
adaptation,
PR(96), 2019, pp. 106996.
Elsevier DOI
1909
Unsupervised domain adaptation, Pseudo labeling,
Feature transformation, Progressive learning, Transfer learning
BibRef
Damodaran, B.B.[Bharath Bhushan],
Flamary, R.[Rémi],
Seguy, V.[Vivien],
Courty, N.[Nicolas],
An Entropic Optimal Transport loss for learning deep neural networks
under label noise in remote sensing images,
CVIU(191), 2020, pp. 102863.
Elsevier DOI
2002
Optimal transport, Entropic Optimal Transport,
Robust deep learning, Noisy labels, Remote sensing
BibRef
Fatras, K.[Kilian],
Damodaran, B.B.[Bharath Bhushan],
Lobry, S.[Sylvain],
Flamary, R.[Rémi],
Tuia, D.[Devis],
Courty, N.[Nicolas],
Wasserstein Adversarial Regularization for Learning With Label Noise,
PAMI(44), No. 10, October 2022, pp. 7296-7306.
IEEE DOI
2209
Noise measurement, Training, Entropy, Neural networks, Task analysis,
Smoothing methods, Semantics, Label noise, optimal transport,
adversarial regularization
BibRef
Damodaran, B.B.[Bharath Bhushan],
Kellenberger, B.[Benjamin],
Flamary, R.[Rémi],
Tuia, D.[Devis],
Courty, N.[Nicolas],
DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised
Domain Adaptation,
ECCV18(II: 467-483).
Springer DOI
1810
BibRef
Li, R.,
Cao, W.,
Wu, S.,
Wong, H.,
Generating Target Image-Label Pairs for Unsupervised Domain
Adaptation,
IP(29), 2020, pp. 7997-8011.
IEEE DOI
2008
Semantics, Adaptation models, Task analysis, Image segmentation,
Data models, Feature extraction, image generation
BibRef
Yan, H.L.[Hong-Liang],
Li, Z.T.[Zhe-Tao],
Wang, Q.L.[Qi-Long],
Li, P.H.[Pei-Hua],
Xu, Y.[Yong],
Zuo, W.M.[Wang-Meng],
Weighted and Class-Specific Maximum Mean Discrepancy for Unsupervised
Domain Adaptation,
MultMed(22), No. 9, September 2020, pp. 2420-2433.
IEEE DOI
2008
Measurement, Adaptation models, Airplanes,
Task analysis, Generative adversarial networks, Degradation,
expectation-maximization algorithms
BibRef
Yan, H.L.[Hong-Liang],
Ding, Y.K.[Yu-Kang],
Li, P.H.[Pei-Hua],
Wang, Q.L.[Qi-Long],
Xu, Y.[Yong],
Zuo, W.M.[Wang-Meng],
Mind the Class Weight Bias:
Weighted Maximum Mean Discrepancy for Unsupervised Domain Adaptation,
CVPR17(945-954)
IEEE DOI
1711
Adaptation models, Computational modeling, Kernel, Manganese,
Measurement, Training
BibRef
Luo, L.,
Chen, L.,
Hu, S.,
Lu, Y.,
Wang, X.,
Discriminative and Geometry-Aware Unsupervised Domain Adaptation,
Cyber(50), No. 9, September 2020, pp. 3914-3927.
IEEE DOI
2008
Data models, Manifolds, Task analysis, Training, Benchmark testing,
Analytical models, Labeling, Data distribution matching,
visual classification
BibRef
Zuo, L.[Lin],
Jing, M.M.[Meng-Meng],
Li, J.J.[Jing-Jing],
Zhu, L.[Lei],
Lu, K.[Ke],
Yang, Y.[Yang],
Challenging tough samples in unsupervised domain adaptation,
PR(110), 2021, pp. 107540.
Elsevier DOI
2011
Domain adaptation, transfer learning, adversarial learning
BibRef
Xu, X.,
He, H.,
Zhang, H.,
Xu, Y.,
He, S.,
Unsupervised Domain Adaptation via Importance Sampling,
CirSysVideo(30), No. 12, December 2020, pp. 4688-4699.
IEEE DOI
2012
Feature extraction, Entropy, Tuning, Estimation, Monte Carlo methods,
Adaptation models, Noise measurement, Domain adaptation,
distribution sampling
BibRef
Wang, X.M.[Xing-Mei],
Sun, B.X.[Bo-Xuan],
Dong, H.B.[Hong-Bin],
Domain-invariant adversarial learning with conditional distribution
alignment for unsupervised domain adaptation,
IET-CV(14), No. 8, December 2020, pp. 642-649.
DOI Link
2012
BibRef
Madadi, Y.[Yeganeh],
Seydi, V.[Vahid],
Nasrollahi, K.[Kamal],
Hosseini, R.[Reshad],
Moeslund, T.B.[Thomas B.],
Deep visual unsupervised domain adaptation for classification tasks:
A survey,
IET-IPR(14), No. 14, December 2020, pp. 3283-3299.
DOI Link
2012
Survey, Domain Adaption.
BibRef
Mancini, M.[Massimiliano],
Porzi, L.[Lorenzo],
Buló, S.R.[Samuel Rota],
Caputo, B.[Barbara],
Ricci, E.[Elisa],
Inferring Latent Domains for Unsupervised Deep Domain Adaptation,
PAMI(43), No. 2, February 2021, pp. 485-498.
IEEE DOI
2101
BibRef
Earlier:
Boosting Domain Adaptation by Discovering Latent Domains,
CVPR18(3771-3780)
IEEE DOI
1812
Adaptation models, Data models,
Neural networks, Training, Visualization, Training data,
object recognition.
Data models, Training
BibRef
Mancini, M.[Massimiliano],
Porzi, L.[Lorenzo],
Cermelli, F.[Fabio],
Caputo, B.[Barbara],
Discovering Latent Domains for Unsupervised Domain Adaptation Through
Consistency,
CIAP19(II:390-401).
Springer DOI
1909
BibRef
Li, H.L.[Hao-Liang],
Wan, R.J.[Ren-Jie],
Wang, S.Q.[Shi-Qi],
Kot, A.C.[Alex C.],
Unsupervised Domain Adaptation in the Wild via Disentangling
Representation Learning,
IJCV(129), No. 2, February 2021, pp. 267-283.
Springer DOI
2102
BibRef
Han, C.[Chao],
Zhou, D.[Deyun],
Xie, Y.[Yu],
Gong, M.[Maoguo],
Lei, Y.[Yu],
Shi, J.[Jiao],
Collaborative representation with curriculum classifier boosting for
unsupervised domain adaptation,
PR(113), 2021, pp. 107802.
Elsevier DOI
2103
Domain adaptation, Collaborative representation,
Curriculum learning, Classifier boosting
BibRef
Zhou, Q.[Qiang],
Zhou, W.[Wen'an],
Wang, S.R.[Shi-Rui],
Cluster adaptation networks for unsupervised domain adaptation,
IVC(108), 2021, pp. 104137.
Elsevier DOI
2104
Domain adaptation, Deep networks, Image classification
BibRef
Chen, C.[Chao],
Fu, Z.H.[Zhi-Hang],
Chen, Z.H.[Zhi-Hong],
Cheng, Z.W.[Zhao-Wei],
Jin, X.Y.[Xin-Yu],
Towards self-similarity consistency and feature discrimination for
unsupervised domain adaptation,
SP:IC(94), 2021, pp. 116232.
Elsevier DOI
2104
Domain adaptation, Self-similarity consistency,
Feature discrimination, Intra-class compactness, Inter-class separability
BibRef
Tang, H.[Hui],
Jia, K.[Kui],
Vicinal and categorical domain adaptation,
PR(115), 2021, pp. 107907.
Elsevier DOI
2104
Unsupervised domain adaptation, Categorical domain adaptation,
Vicinal domain adaptation, Cross-domain weighting, Domain augmentation
BibRef
Wang, J.[Jing],
Chen, J.H.[Jia-Hong],
Lin, J.Z.[Jian-Zhe],
Sigal, L.[Leonid],
de Silva, C.W.[Clarence W.],
Discriminative feature alignment: Improving transferability of
unsupervised domain adaptation by Gaussian-guided latent alignment,
PR(116), 2021, pp. 107943.
Elsevier DOI
2106
Domain adaptation, Information theory
BibRef
Zhang, K.[Kuangen],
Chen, J.H.[Jia-Hong],
Wang, J.[Jing],
Leng, Y.[Yuquan],
de Silva, C.W.[Clarence W.],
Fu, C.L.[Cheng-Long],
Gaussian-guided feature alignment for unsupervised cross-subject
adaptation,
PR(122), 2022, pp. 108332.
Elsevier DOI
2112
Domain adaptation, Feature alignment,
Human activity recognition, Human intent recognition, Sensor fusion
BibRef
Li, S.[Shuang],
Liu, C.H.[Chi Harold],
Lin, Q.X.[Qiu-Xia],
Wen, Q.[Qi],
Su, L.M.[Li-Min],
Huang, G.[Gao],
Ding, Z.M.[Zheng-Ming],
Deep Residual Correction Network for Partial Domain Adaptation,
PAMI(43), No. 7, July 2021, pp. 2329-2344.
IEEE DOI
2106
Task analysis, Deep learning, Visualization, Learning systems,
Training, Probability distribution, Measurement,
fine-grained visual recognition
BibRef
Ding, Z.M.[Zheng-Ming],
Li, S.[Sheng],
Shao, M.[Ming],
Fu, Y.[Yun],
Graph Adaptive Knowledge Transfer for Unsupervised Domain Adaptation,
ECCV18(II: 36-52).
Springer DOI
1810
BibRef
Deng, W.X.[Wan-Xia],
Liao, Q.[Qing],
Zhao, L.J.[Ling-Jun],
Guo, D.[Deke],
Kuang, G.Y.[Gang-Yao],
Hu, D.[Dewen],
Liu, L.[Li],
Joint Clustering and Discriminative Feature Alignment for
Unsupervised Domain Adaptation,
IP(30), 2021, pp. 7842-7855.
IEEE DOI
2109
BibRef
And: A3, A1, A5, A6, A7, Only:
Transferable Discriminative Feature Mining for Unsupervised Domain
Adaptation,
ICIP21(1259-1263)
IEEE DOI
2201
Feature extraction, Task analysis, Image reconstruction, Training,
Image coding, Deep learning, Data mining, Domain adaptation,
semisupervised learning.
Adaptation models, Minimization, unsupervised learning,
image classification
BibRef
Deng, W.X.[Wan-Xia],
Su, Z.[Zhuo],
Qiu, Q.[Qiang],
Zhao, L.J.[Ling-Jun],
Kuang, G.Y.[Gang-Yao],
Pietikäinen, M.[Matti],
Xiao, H.X.[Hua-Xin],
Liu, L.[Li],
Deep ladder reconstruction-classification network for unsupervised
domain adaptation,
PRL(152), 2021, pp. 398-405.
Elsevier DOI
2112
Domain adaptation, Deep learning,
Deep convolutional neural network, Autoencoder, Unsupervised learning
BibRef
Dai, P.Y.[Ping-Yang],
Chen, P.X.[Pei-Xian],
Wu, Q.[Qiong],
Hong, X.P.[Xiao-Peng],
Ye, Q.X.[Qi-Xiang],
Tian, Q.[Qi],
Lin, C.W.[Chia-Wen],
Ji, R.R.[Rong-Rong],
Disentangling Task-Oriented Representations for Unsupervised Domain
Adaptation,
IP(31), 2022, pp. 1012-1026.
IEEE DOI
2201
Task analysis, Adaptation models, Image color analysis,
Vehicle dynamics, Linear programming, Image retrieval, Semantics,
person re-identification
BibRef
Luo, Y.W.[You-Wei],
Ren, C.X.[Chuan-Xian],
Dai, D.Q.[Dao-Qing],
Yan, H.[Hong],
Unsupervised Domain Adaptation via Discriminative Manifold
Propagation,
PAMI(44), No. 3, March 2022, pp. 1653-1669.
IEEE DOI
2202
Manifolds, Machine learning, Measurement, Training, Prototypes,
Task analysis, Dictionaries, Unsupervised domain adaptation,
manifold alignment
BibRef
Kang, G.L.[Guo-Liang],
Jiang, L.[Lu],
Wei, Y.C.[Yun-Chao],
Yang, Y.[Yi],
Hauptmann, A.G.[Alexander G.],
Contrastive Adaptation Network for Single- and Multi-Source Domain
Adaptation,
PAMI(44), No. 4, April 2022, pp. 1793-1804.
IEEE DOI
2203
BibRef
Earlier: A1, A2, A4, A5, Only:
Contrastive Adaptation Network for Unsupervised Domain Adaptation,
CVPR19(4888-4897).
IEEE DOI
2002
Training, Neural networks, Adaptation models, Benchmark testing,
Measurement, Manuals, Estimation, Contrastive, domain adaptation,
multi-source
BibRef
Ren, C.X.[Chuan-Xian],
Liu, Y.H.[Yong-Hui],
Zhang, X.W.[Xi-Wen],
Huang, K.K.[Ke-Kun],
Multi-Source Unsupervised Domain Adaptation via Pseudo Target Domain,
IP(31), 2022, pp. 2122-2135.
IEEE DOI
2203
Task analysis, Training, Feature extraction, Adaptation models,
Neural networks, Manganese, Predictive models, matching normalization layer
BibRef
Ge, P.F.[Peng-Fei],
Ren, C.X.[Chuan-Xian],
Xu, X.L.[Xiao-Lin],
Yan, H.[Hong],
Unsupervised Domain Adaptation via Deep Conditional Adaptation
Network,
PR(134), 2023, pp. 109088.
Elsevier DOI
2212
Deep learning, Domain adaptation, Feature extraction,
Conditional maximum mean discrepancy, Kernel method
BibRef
Chen, T.[Tao],
Wang, S.H.[Shui-Hua],
Wang, Q.[Qiong],
Zhang, Z.[Zheng],
Xie, G.S.[Guo-Sen],
Tang, Z.[Zhenmin],
Enhanced Feature Alignment for Unsupervised Domain Adaptation of
Semantic Segmentation,
MultMed(24), 2022, pp. 1042-1054.
IEEE DOI
2203
Training, Charge coupled devices, Convolutional codes, Semantics,
Feature extraction, Distortion, Adversarial machine learning,
semantic segmentation
BibRef
Lu, Y.[Yuwu],
Li, D.[Desheng],
Wang, W.J.[Wen-Jing],
Lai, Z.H.[Zhi-Hui],
Zhou, J.[Jie],
Li, X.L.[Xue-Long],
Discriminative Invariant Alignment for Unsupervised Domain Adaptation,
MultMed(24), No. 2022, pp. 1871-1882.
IEEE DOI
2204
Task analysis, Feature extraction, Manifolds, Degradation,
Neural networks, Kernel, Data structures, Domain adaptation,
maximum margin criterion
BibRef
Deng, W.[Wanxia],
Zhao, L.[Lingjun],
Liao, Q.[Qing],
Guo, D.[Deke],
Kuang, G.Y.[Gang-Yao],
Hu, D.[Dewen],
Pietikäinen, M.[Matti],
Liu, L.[Li],
Informative Feature Disentanglement for Unsupervised Domain
Adaptation,
MultMed(24), 2022, pp. 2407-2421.
IEEE DOI
2205
Task analysis, Measurement, Feature extraction, Wheels,
Image reconstruction, Image color analysis, Adaptation models,
unsupervised learning
BibRef
Guan, D.[Dayan],
Huang, J.X.[Jia-Xing],
Xiao, A.[Aoran],
Lu, S.J.[Shi-Jian],
Cao, Y.P.[Yan-Peng],
Uncertainty-Aware Unsupervised Domain Adaptation in Object Detection,
MultMed(24), 2022, pp. 2502-2514.
IEEE DOI
2205
Proposals, Uncertainty, Object detection, Entropy,
Feature extraction, Detectors, Convolutional neural networks,
curriculum learning
BibRef
Zhang, C.[Chong],
Li, Z.X.[Zong-Xian],
Liu, J.J.[Jing-Jing],
Peng, P.X.[Pei-Xi],
Ye, Q.X.[Qi-Xiang],
Lu, S.J.[Shi-Jian],
Huang, T.J.[Tie-Jun],
Tian, Y.H.[Yong-Hong],
Self-Guided Adaptation: Progressive Representation Alignment for
Domain Adaptive Object Detection,
MultMed(24), 2022, pp. 2246-2258.
IEEE DOI
2205
Adaptation models, Object detection, Feature extraction, Detectors,
Proposals, Kernel, Hilbert space, Self-Guided Adaptation,
Domain Adaptive Object Detection
BibRef
Xu, Y.[Yayun],
Yan, H.[Hua],
Cycle-reconstructive subspace learning with class discriminability
for unsupervised domain adaptation,
PR(129), 2022, pp. 108700.
Elsevier DOI
2206
Domain adaptation, Subspace learning, Transfer learning, Knowledge transfer
BibRef
Moon, J.[Jihoon],
Das, D.[Debasmit],
Lee, C.S.G.[C. S. George],
A Multistage Framework With Mean Subspace Computation and Recursive
Feedback for Online Unsupervised Domain Adaptation,
IP(31), 2022, pp. 4622-4636.
IEEE DOI
2207
Manifolds, Task analysis, Adaptation models, Data models,
Computational modeling, Transforms, Sun, Mean subspace,
unsupervised domain adaptation
BibRef
Zhang, J.M.[Jia-Ming],
Ma, C.X.[Chao-Xiang],
Yang, K.[Kailun],
Roitberg, A.[Alina],
Peng, K.[Kunyu],
Stiefelhagen, R.[Rainer],
Transfer Beyond the Field of View: Dense Panoramic Semantic
Segmentation via Unsupervised Domain Adaptation,
ITS(23), No. 7, July 2022, pp. 9478-9491.
IEEE DOI
2207
Image segmentation, Semantics, Cameras, Adaptation models,
Training data, Task analysis, Standards, Semantic segmentation,
intelligent vehicles
BibRef
Tao, X.F.[Xue-Feng],
Kong, J.[Jun],
Jiang, M.[Min],
Liu, T.S.[Tian-Shan],
Unsupervised Domain Adaptation by Multi-Loss Gap Minimization
Learning for Person Re-Identification,
CirSysVideo(32), No. 7, July 2022, pp. 4404-4416.
IEEE DOI
2207
Cameras, Adaptation models, Minimization, Training, Task analysis,
Semantics, Data models, Unsupervised domain adaptation,
minimum camera discrepancy loss
BibRef
Jin, X.[Xin],
Lan, C.L.[Cui-Ling],
Zeng, W.J.[Wen-Jun],
Chen, Z.B.[Zhi-Bo],
Style Normalization and Restitution for Domain Generalization and
Adaptation,
MultMed(24), 2022, pp. 3636-3651.
IEEE DOI
2207
Task analysis, Signal to noise ratio, Training, Feature extraction,
Entropy, Testing, Lighting, unsupervised domain adaptation
BibRef
Lu, J.[Jiang],
Li, L.[Lei],
Zhang, C.S.[Chang-Shui],
Self-Reinforcing Unsupervised Matching,
PAMI(44), No. 8, August 2022, pp. 4404-4418.
IEEE DOI
2207
Visualization, Task analysis, Heuristic algorithms,
Dynamic programming, Manuals, Tensors, Pattern analysis
BibRef
Meng, M.[Min],
Wu, Z.[Zhuanghui],
Liang, T.[Tianyou],
Yu, J.[Jun],
Wu, J.[Jigang],
Exploring Fine-Grained Cluster Structure Knowledge for Unsupervised
Domain Adaptation,
CirSysVideo(32), No. 8, August 2022, pp. 5481-5494.
IEEE DOI
2208
Representation learning, Adaptation models, Predictive models,
Measurement, Feature extraction, Data models, Data visualization,
structural centroid-based label prediction
BibRef
Bucci, S.[Silvia],
d'Innocente, A.[Antonio],
Liao, Y.J.[Yu-Jun],
Carlucci, F.M.[Fabio M.],
Caputo, B.[Barbara],
Tommasi, T.[Tatiana],
Self-Supervised Learning Across Domains,
PAMI(44), No. 9, September 2022, pp. 5516-5528.
IEEE DOI
2208
Task analysis, Visualization, Indexes, Adaptation models,
Data models, Training, Image recognition, Self-supervision,
multi-task learning
BibRef
Xu, M.Q.[Meng-Qiu],
Wu, M.[Ming],
Chen, K.X.[Kai-Xin],
Zhang, C.[Chuang],
Guo, J.[Jun],
The Eyes of the Gods: A Survey of Unsupervised Domain Adaptation
Methods Based on Remote Sensing Data,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link
2209
Survey, Domain Adaptation.
BibRef
Li, W.[Weikai],
Chen, S.C.[Song-Can],
Unsupervised domain adaptation with progressive adaptation of
subspaces,
PR(132), 2022, pp. 108918.
Elsevier DOI
2209
Unsupervised domain adaptation, Partial domain adaptation,
Subspace learning, Pseudo label
BibRef
Kwak, G.H.[Geun-Ho],
Park, N.W.[No-Wook],
Unsupervised Domain Adaptation with Adversarial Self-Training for
Crop Classification Using Remote Sensing Images,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link
2209
BibRef
Liang, J.[Jian],
Hu, D.P.[Da-Peng],
Wang, Y.[Yunbo],
He, R.[Ran],
Feng, J.S.[Jia-Shi],
Source Data-Absent Unsupervised Domain Adaptation Through Hypothesis
Transfer and Labeling Transfer,
PAMI(44), No. 11, November 2022, pp. 8602-8617.
IEEE DOI
2210
Task analysis, Labeling, Adaptation models, Encoding,
Image reconstruction, Feature extraction, Data models,
model reuse
BibRef
Han, Z.Y.[Zhong-Yi],
Sun, H.L.[Hao-Liang],
Yin, Y.L.[Yi-Long],
Learning Transferable Parameters for Unsupervised Domain Adaptation,
IP(31), 2022, pp. 6424-6439.
IEEE DOI
2211
Feature extraction, Training, Task analysis, Linear programming,
Estimation, Data mining, Supervised learning, keypoint detection
BibRef
Qin, C.[Can],
Wang, L.C.[Li-Chen],
Ma, Q.Q.[Qian-Qian],
Yin, Y.[Yu],
Wang, H.[Huan],
Fu, Y.[Yun],
Semi-Supervised Domain Adaptive Structure Learning,
IP(31), 2022, pp. 7179-7190.
IEEE DOI
2212
Training, Generators, Feature extraction, Correlation,
Adversarial machine learning, Minimization, Entropy,
adaptive structure learning
BibRef
Lu, Y.[Yuwu],
Luo, X.P.[Xing-Ping],
Wen, J.J.[Jia-Jun],
Lai, Z.H.[Zhi-Hui],
Li, X.L.[Xue-Long],
Cross-domain structure learning for visual data recognition,
PR(134), 2023, pp. 109127.
Elsevier DOI
2212
Domain adaptation, Cross-domain, Classwise structure learning,
Sample reweighting
BibRef
Zhang, W.W.[Wen-Wen],
Wang, J.G.[Jian-Gong],
Wang, Y.T.[Yu-Tong],
Wang, F.Y.[Fei-Yue],
ParaUDA: Invariant Feature Learning With Auxiliary Synthetic Samples
for Unsupervised Domain Adaptation,
ITS(23), No. 11, November 2022, pp. 20217-20229.
IEEE DOI
2212
Adaptation models, Representation learning, Feature extraction,
Task analysis, Semantics, Generative adversarial networks,
domain-invariant representation
BibRef
Du, Y.J.[Yong-Jie],
Zhou, D.[Deyun],
Xie, Y.[Yu],
Lei, Y.[Yu],
Shi, J.[Jiao],
Prototype-Guided Feature Learning for Unsupervised Domain Adaptation,
PR(135), 2023, pp. 109154.
Elsevier DOI
2212
Unsupervised domain adaptation, Class prototype,
Pseudo labeling, Label filtering
BibRef
Tian, Q.[Qing],
Zhu, Y.[Yanan],
Sun, H.[Heyang],
Chen, S.C.[Song-Can],
Yin, H.J.[Hu-Jun],
Unsupervised Domain Adaptation Through Dynamically Aligning Both the
Feature and Label Spaces,
CirSysVideo(32), No. 12, December 2022, pp. 8562-8573.
IEEE DOI
2212
Feature extraction, Adaptation models, Generators,
Weight measurement, Task analysis, Heuristic algorithms, Training,
adversarial discrimination adaptation
BibRef
Wang, W.X.[Wen-Xu],
Shen, Z.[Zhencai],
Li, D.[Daoliang],
Zhong, P.[Ping],
Chen, Y.Y.[Ying-Yi],
Probability-Based Graph Embedding Cross-Domain and Class
Discriminative Feature Learning for Domain Adaptation,
IP(32), 2023, pp. 72-87.
IEEE DOI
2301
Representation learning, Aquaculture, Adaptation models,
Technological innovation, Benchmark testing, Manifolds, Kernel,
unsupervised domain adaptation
BibRef
Xia, H.F.[Hai-Feng],
Jing, T.[Taotao],
Ding, Z.M.[Zheng-Ming],
Maximum Structural Generation Discrepancy for Unsupervised Domain
Adaptation,
PAMI(45), No. 3, March 2023, pp. 3434-3445.
IEEE DOI
2302
Feature extraction, Collaboration, Training, Semantics,
Task analysis, Bridges, Annotations,
collaborative translation
BibRef
Wang, M.Z.[Meng-Zhu],
Wang, S.S.[Shan-Shan],
Wang, W.[Wei],
Shen, L.[Li],
Zhang, X.[Xiang],
Lan, L.[Long],
Luo, Z.G.[Zhi-Gang],
Reducing bi-level feature redundancy for unsupervised domain
adaptation,
PR(137), 2023, pp. 109319.
Elsevier DOI
2302
Feature redundancy, Unsupervised domain adaptation, Whitening, Orthogonality
BibRef
Wang, J.[Jie],
Zhang, X.L.[Xiao-Lei],
Improving pseudo labels with intra-class similarity for unsupervised
domain adaptation,
PR(138), 2023, pp. 109379.
Elsevier DOI
2303
Unsupervised domain adaptation, Intra-class similarity,
Spanning trees, Pseudo labels
BibRef
Pei, J.B.[Jiang-Bo],
Jiang, Z.Q.[Zhu-Qing],
Men, A.[Aidong],
Chen, L.[Liang],
Liu, Y.[Yang],
Chen, Q.C.[Qing-Chao],
Uncertainty-Induced Transferability Representation for Source-Free
Unsupervised Domain Adaptation,
IP(32), 2023, pp. 2033-2048.
IEEE DOI
2304
Data models, Adaptation models, Semantics, Uncertainty, Training,
Calibration, Measurement uncertainty,
adaptive machine learning
BibRef
Tan, A.D.[An-Dong],
Hanselmann, N.[Niklas],
Ding, S.[Shuxiao],
Tombari, F.[Federico],
Cordts, M.[Marius],
Unsupervised Domain Adaptive Object Detection with Class Label Shift
Weighted Local Features,
LLID22(118-133).
Springer DOI
2304
BibRef
Rawat, A.[Abhay],
Dua, I.[Isha],
Gupta, S.[Saurav],
Tallamraju, R.[Rahul],
Semi-supervised Domain Adaptation by Similarity Based Pseudo-label
Injection,
LLID22(150-166).
Springer DOI
2304
BibRef
Mahapatra, D.[Dwarikanath],
Korevaar, S.[Steven],
Bozorgtabar, B.[Behzad],
Tennakoon, R.[Ruwan],
Unsupervised Domain Adaptation Using Feature Disentanglement and Gcns
for Medical Image Classification,
MIA-COVID19D22(735-748).
Springer DOI
2304
BibRef
Wang, Y.M.[Yu-Miao],
Feng, L.[Luwei],
Zhang, Z.[Zhou],
Tian, F.[Feng],
An unsupervised domain adaptation deep learning method for spatial
and temporal transferable crop type mapping using Sentinel-2 imagery,
PandRS(199), 2023, pp. 102-117.
Elsevier DOI
2305
Crop type mapping, Unsupervised domain adaptation,
Time-series imagery, Transfer learning
BibRef
Ouyang, J.J.[Jia-Jun],
Lv, Q.X.[Qing-Xuan],
Zhang, S.[Shu],
Dong, J.Y.[Jun-Yu],
Energy Transfer Contrast Network for Unsupervised Domain Adaption,
MMMod23(II: 115-126).
Springer DOI
2304
BibRef
Yang, Y.[Yiju],
Kim, T.[Taejoon],
Wang, G.H.[Guang-Hui],
Multiple Classifiers Based Adversarial Training for Unsupervised
Domain Adaptation,
CRV22(40-47)
IEEE DOI
2301
Training, Codes, Computational efficiency,
Classification algorithms, Task analysis, Time complexity, Robots,
maximum classifier discrepancy
BibRef
Tomaszewska, P.[Paulina],
Lampert, C.H.[Christoph H.],
Lightweight Conditional Model Extrapolation for Streaming Data under
Class-Prior Shift,
ICPR22(2128-2134)
IEEE DOI
2212
Training, Measurement, Extrapolation, Adaptation models,
Social networking (online), Computational modeling, Blogs,
class-prior shift
BibRef
Choi, J.[Jinwoo],
Huang, J.B.[Jia-Bin],
Sharma, G.[Gaurav],
Self-Supervised Cross-Video Temporal Learning for Unsupervised Video
Domain Adaptation,
ICPR22(3464-3470)
IEEE DOI
2212
Training, Adaptation models, Self-supervised learning,
Benchmark testing, Cognition, Task analysis
BibRef
Truong, T.D.[Thanh-Dat],
Chappa, R.T.N.[Ravi Teja Nvs],
Nguyen, X.B.[Xuan-Bac],
Le, N.[Ngan],
Dowling, A.P.G.[Ashley P.G.],
Luu, K.[Khoa],
OTAdapt: Optimal Transport-based Approach For Unsupervised Domain
Adaptation,
ICPR22(2850-2856)
IEEE DOI
2212
Measurement, Webcams, Insects, Topology, Proposals
BibRef
Lin, W.[Wei],
Kukleva, A.[Anna],
Sun, K.[Kunyang],
Possegger, H.[Horst],
Kuehne, H.[Hilde],
Bischof, H.[Horst],
CycDA: Unsupervised Cycle Domain Adaptation to Learn from Image to
Video,
ECCV22(III:698-715).
Springer DOI
2211
BibRef
Hu, C.[Conghui],
Lee, G.H.[Gim Hee],
Feature Representation Learning for Unsupervised Cross-Domain Image
Retrieval,
ECCV22(XXXVII:529-544).
Springer DOI
2211
BibRef
Sun, T.[Tao],
Lu, C.[Cheng],
Ling, H.B.[Hai-Bin],
Prior Knowledge Guided Unsupervised Domain Adaptation,
ECCV22(XXXIII:639-655).
Springer DOI
2211
BibRef
Na, J.[Jaemin],
Han, D.Y.[Dong-Yoon],
Chang, H.J.[Hyung Jin],
Hwang, W.J.[Won-Jun],
Contrastive Vicinal Space for Unsupervised Domain Adaptation,
ECCV22(XXXIV:92-110).
Springer DOI
2211
BibRef
Lee, E.S.[Eun Sun],
Kim, J.[Junho],
Park, S.[SangWon],
Kim, Y.M.[Young Min],
MoDA: Map Style Transfer for Self-supervised Domain Adaptation of
Embodied Agents,
ECCV22(XXIX:338-354).
Springer DOI
2211
BibRef
Lu, X.[Xiaohu],
Radha, H.[Hayder],
Strong-Weak Integrated Semi-Supervision for Unsupervised Domain
Adaptation,
ICIP22(2226-2230)
IEEE DOI
2211
Training, Benchmark testing, Semisupervised learning,
Domain adaptation, Semi-supervised, Adversarial logit, Intra-class divergence
BibRef
Lin, H.B.[Hong-Bin],
Zhang, Y.F.[Yi-Fan],
Qiu, Z.[Zhen],
Niu, S.C.[Shuai-Cheng],
Gan, C.[Chuang],
Liu, Y.X.[Yan-Xia],
Tan, M.K.[Ming-Kui],
Prototype-Guided Continual Adaptation for Class-Incremental
Unsupervised Domain Adaptation,
ECCV22(XXXIII:351-368).
Springer DOI
2211
BibRef
Wu, C.E.[Cheng-En],
Lai, F.[Farley],
Hu, Y.H.[Yu Hen],
Kadav, A.[Asim],
Self-supervised Video Representation Learning with Cascade Positive
Retrieval,
L3D-IVU22(4069-4078)
IEEE DOI
2210
Representation learning, Training, Visualization, Upper bound,
Scalability, Transfer learning, Self-supervised learning
BibRef
Wei, C.[Chen],
Fan, H.Q.[Hao-Qi],
Xie, S.[Saining],
Wu, C.Y.[Chao-Yuan],
Yuille, A.L.[Alan L.],
Feichtenhofer, C.[Christoph],
Masked Feature Prediction for Self-Supervised Visual Pre-Training,
CVPR22(14648-14658)
IEEE DOI
2210
Deep learning, Visualization, Histograms, Computational modeling,
Transfer learning, Predictive models, Video analysis and understanding
BibRef
Lu, C.J.[Chang-Jie],
Zheng, S.[Shen],
Gupta, G.[Gaurav],
Unsupervised Domain Adaptation for Cardiac Segmentation: Towards
Structure Mutual Information Maximization,
Precognition22(2587-2596)
IEEE DOI
2210
Image segmentation, Adaptation models, Estimation,
Benchmark testing, Pattern recognition, Task analysis
BibRef
Liu, Q.[Qing],
Kortylewski, A.[Adam],
Zhang, Z.S.[Zhi-Shuai],
Li, Z.Z.[Zi-Zhang],
Guo, M.Q.[Meng-Qi],
Liu, Q.H.[Qi-Hao],
Yuan, X.D.[Xiao-Ding],
Mu, J.[Jiteng],
Qiu, W.[Weichao],
Yuille, A.L.[Alan L.],
Learning Part Segmentation through Unsupervised Domain Adaptation
from Synthetic Vehicles,
CVPR22(19118-19129)
IEEE DOI
2210
Deep learning, Image segmentation, Solid modeling,
Adaptation models, Costs, Benchmark testing,
Self- semi- meta- Transfer/low-shot/long-tail learning
BibRef
Zhang, J.Y.[Jing-Yi],
Huang, J.X.[Jia-Xing],
Tian, Z.C.[Zi-Chen],
Lu, S.J.[Shi-Jian],
Spectral Unsupervised Domain Adaptation for Visual Recognition,
CVPR22(9819-9830)
IEEE DOI
2210
Visualization, Image segmentation, Semantics, Object detection,
Transformers, Pattern recognition,
Self- semi- meta- Transfer/low-shot/long-tail learning
BibRef
Ye, J.J.[Jun-Jie],
Fu, C.H.[Chang-Hong],
Zheng, G.Z.[Guang-Ze],
Paudel, D.P.[Danda Pani],
Chen, G.[Guang],
Unsupervised Domain Adaptation for Nighttime Aerial Tracking,
CVPR22(8886-8895)
IEEE DOI
2210
Training, Adaptation models, Target tracking, Benchmark testing,
Transformers, Feature extraction, Robustness, Motion and tracking,
Transfer/low-shot/long-tail learning
BibRef
Fan, H.[Hehe],
Chang, X.J.[Xiao-Jun],
Zhang, W.[Wanyue],
Cheng, Y.[Yi],
Sun, Y.[Ying],
Kankanhalli, M.[Mohan],
Self-Supervised Global-Local Structure Modeling for Point Cloud
Domain Adaptation with Reliable Voted Pseudo Labels,
CVPR22(6367-6376)
IEEE DOI
2210
Point cloud compression, Training, Representation learning,
Adaptation models, Self-supervised learning, Predictive models,
Transfer/low-shot/long-tail learning
BibRef
Huang, J.X.[Jia-Xing],
Guan, D.[Dayan],
Xiao, A.[Aoran],
Lu, S.J.[Shi-Jian],
Shao, L.[Ling],
Category Contrast for Unsupervised Domain Adaptation in Visual Tasks,
CVPR22(1193-1204)
IEEE DOI
2210
Representation learning, Visualization, Adaptation models,
Dictionaries, Computational modeling, Semantics, Segmentation,
Transfer/low-shot/long-tail learning
BibRef
Saito, K.[Kuniaki],
Kim, D.H.[Dong-Hyun],
Teterwak, P.[Piotr],
Sclaroff, S.[Stan],
Darrell, T.J.[Trevor J.],
Saenko, K.[Kate],
Tune it the Right Way: Unsupervised Validation of Domain Adaptation
via Soft Neighborhood Density,
ICCV21(9164-9173)
IEEE DOI
2203
Training, Image segmentation, Codes, Density measurement,
Computational modeling, Semantics, Recognition and classification
BibRef
Gu, Q.Q.[Qi-Qi],
Zhou, Q.Y.[Qian-Yu],
Xu, M.H.[Ming-Hao],
Feng, Z.Y.[Zheng-Yang],
Cheng, G.L.[Guang-Liang],
Lu, X.Q.[Xue-Quan],
Shi, J.P.[Jian-Ping],
Ma, L.Z.[Li-Zhuang],
PIT: Position-Invariant Transform for Cross-FoV Domain Adaptation,
ICCV21(8741-8750)
IEEE DOI
2203
Training, Image segmentation, Codes, Semantics, Lighting, Transforms,
Transfer/Low-shot/Semi/Unsupervised Learning,
Vision for robotics and autonomous vehicles
BibRef
Yang, J.[Jinyu],
Li, C.Y.[Chun-Yuan],
An, W.Z.[Wei-Zhi],
Ma, H.[Hehuan],
Guo, Y.Z.[Yu-Zhi],
Rong, Y.[Yu],
Zhao, P.[Peilin],
Huang, J.Z.[Jun-Zhou],
Exploring Robustness of Unsupervised Domain Adaptation in Semantic
Segmentation,
ICCV21(9174-9183)
IEEE DOI
2203
Resistance, Deep learning, Image segmentation,
Perturbation methods, Semantics, Neural networks,
grouping and shape
BibRef
Liu, X.F.[Xiao-Feng],
Li, S.[Site],
Ge, Y.[Yubin],
Ye, P.Y.[Peng-Yi],
You, J.[Jane],
Lu, J.[Jun],
Recursively Conditional Gaussian for Ordinal Unsupervised Domain
Adaptation,
ICCV21(744-753)
IEEE DOI
2203
Adaptation models, Pathology, Scalability, Estimation,
Aerospace electronics, Noise measurement,
Representation learning
BibRef
Lee, E.S.[Eun Sun],
Kim, J.[Junho],
Kim, Y.M.[Young Min],
Self-Supervised Domain Adaptation for Visual Navigation with Global
Map Consistency,
WACV22(1868-1877)
IEEE DOI
2202
Training, Location awareness, Visualization, Navigation,
Graphics processing units, Data models, Trajectory, Vision for Robotics
BibRef
Yoon, J.[Jeongbeen],
Kang, D.[Dahyun],
Cho, M.[Minsu],
Semi-supervised Domain Adaptation via Sample-to-Sample
Self-Distillation,
WACV22(1686-1695)
IEEE DOI
2202
Training, Bridges, Adaptation models,
Benchmark testing, Standards, Transfer,
Scene Understanding
BibRef
Agarwal, P.[Peshal],
Paudel, D.P.[Danda Pani],
Zaech, J.N.[Jan-Nico],
Van Gool, L.J.[Luc J.],
Unsupervised Robust Domain Adaptation without Source Data,
WACV22(2805-2814)
IEEE DOI
2202
Representation learning, Adaptation models,
Computational modeling, Perturbation methods, Predictive models,
Semi- and Un- supervised Learning
BibRef
Ahmed, W.[Waqar],
Morerio, P.[Pietro],
Murino, V.[Vittorio],
Cleaning Noisy Labels by Negative Ensemble Learning for Source-Free
Unsupervised Domain Adaptation,
WACV22(356-365)
IEEE DOI
2202
Training, Data privacy, Filtering,
Stochastic processes, Benchmark testing, Cleaning, Transfer,
Semi- and Un- supervised Learning Deep Learning ->
Efficient Training and Inference Methods for Networks
BibRef
Robbiano, L.[Luca],
Rahman, M.R.U.[Muhammad Rameez Ur],
Galasso, F.[Fabio],
Caputo, B.[Barbara],
Carlucci, F.M.[Fabio Maria],
Adversarial Branch Architecture Search for Unsupervised Domain
Adaptation,
WACV22(1008-1018)
IEEE DOI
2202
Bridges, Visualization, Adaptation models,
Object Detection/Recognition/Categorization
BibRef
Wang, J.[Jie],
Zhong, C.L.[Chao-Liang],
Feng, C.[Cheng],
Sun, J.[Jun],
Ide, M.[Masaru],
Yokota, Y.[Yasuto],
Dual-Consistency Self-Training for Unsupervised Domain Adaptation,
ICIP21(1529-1533)
IEEE DOI
2201
Image processing, Prototypes, Benchmark testing, Task analysis,
Standards, Consistency, Self-training, Unsupervised Domain Adaptation
BibRef
Zhang, Y.[Youshan],
Davison, B.D.[Brian D.],
Enhanced Separable Disentanglement for Unsupervised Domain Adaptation,
ICIP21(784-788)
IEEE DOI
2201
Training, Adaptation models, Benchmark testing,
Electrostatic discharges, Task analysis, Image reconstruction,
Domain discriminator
BibRef
Hou, Y.Z.[Yun-Zhong],
Zheng, L.[Liang],
Visualizing Adapted Knowledge in Domain Transfer,
CVPR21(13819-13828)
IEEE DOI
2111
Adaptation models, Computational modeling, Force,
Data visualization, Data models, Pattern recognition
BibRef
Melas-Kyriazi, L.[Luke],
Manrai, A.K.[Arjun K.],
PixMatch: Unsupervised Domain Adaptation via Pixelwise Consistency
Training,
CVPR21(12430-12440)
IEEE DOI
2111
Training, Adaptation models, Image segmentation,
Perturbation methods, Computational modeling, Semantics
BibRef
Xiao, N.[Ni],
Zhang, L.[Lei],
Dynamic Weighted Learning for Unsupervised Domain Adaptation,
CVPR21(15237-15246)
IEEE DOI
2111
Training, Adaptation models, Codes,
Benchmark testing, Pattern recognition, Task analysis
BibRef
Wei, G.Q.[Guo-Qiang],
Lan, C.L.[Cui-Ling],
Zeng, W.J.[Wen-Jun],
Chen, Z.B.[Zhi-Bo],
MetaAlign: Coordinating Domain Alignment and Classification for
Unsupervised Domain Adaptation,
CVPR21(16638-16648)
IEEE DOI
2111
Training, Object detection,
Adversarial machine learning, Pattern recognition, Task analysis, Optimization
BibRef
Saha, S.[Suman],
Obukhov, A.[Anton],
Paudel, D.P.[Danda Pani],
Kanakis, M.[Menelaos],
Chen, Y.H.[Yu-Hua],
Georgoulis, S.[Stamatios],
Van Gool, L.J.[Luc J.],
Learning to Relate Depth and Semantics for Unsupervised Domain
Adaptation,
CVPR21(8193-8203)
IEEE DOI
2111
Training, Visualization, Correlation, Semantics, Neural networks,
Estimation, Predictive models
BibRef
Sharma, A.[Astuti],
Kalluri, T.[Tarun],
Chandraker, M.[Manmohan],
Instance Level Affinity-Based Transfer for Unsupervised Domain
Adaptation,
CVPR21(5357-5367)
IEEE DOI
2111
Training, Adaptation models, Benchmark testing,
Drives, Data models, Numerical models
BibRef
Du, Z.[Zhekai],
Li, J.J.[Jing-Jing],
Su, H.Z.[Hong-Zu],
Zhu, L.[Lei],
Lu, K.[Ke],
Cross-Domain Gradient Discrepancy Minimization for Unsupervised
Domain Adaptation,
CVPR21(3936-3945)
IEEE DOI
2111
Semantics, Minimization, Adversarial machine learning,
Pattern recognition, Reliability
BibRef
Na, J.[Jaemin],
Jung, H.[Heechul],
Chang, H.J.[Hyung Jin],
Hwang, W.J.[Won-Jun],
FixBi: Bridging Domain Spaces for Unsupervised Domain Adaptation,
CVPR21(1094-1103)
IEEE DOI
2111
Adaptation models, Benchmark testing,
Predictive models, Pattern recognition, Standards
BibRef
Zhang, Y.[Youshan],
Davison, B.D.[Brian D.],
Efficient Pre-trained Features and Recurrent Pseudo-Labeling in
Unsupervised Domain Adaptation,
LLID21(2713-2722)
IEEE DOI
2109
Training, Adaptation models, Computational modeling, Benchmark testing
BibRef
Singh, A.[Anurag],
Doraiswamy, N.[Naren],
Takamuku, S.[Sawa],
Bhalerao, M.[Megh],
Dutta, T.[Titir],
Biswas, S.[Soma],
Chepuri, A.[Aditya],
Vengatesan, B.[Balasubramanian],
Natori, N.[Naotake],
Improving Semi-Supervised Domain Adaptation Using Effective Target
Selection and Semantics,
LLID21(2703-2712)
IEEE DOI
2109
Uncertainty, Computational modeling,
Semantics, Prototypes, Manuals
BibRef
Chao, C.H.[Chen-Hao],
Cheng, B.W.[Bo-Wun],
Lee, C.Y.[Chun-Yi],
Rethinking Ensemble-Distillation for Semantic Segmentation Based
Unsupervised Domain Adaption,
LLID21(2610-2620)
IEEE DOI
2109
Learning systems, Semantics,
Benchmark testing, Robustness, Pattern recognition
BibRef
Barbato, F.[Francesco],
Toldo, M.[Marco],
Michieli, U.[Umberto],
Zanuttigh, P.[Pietro],
Latent Space Regularization for Unsupervised Domain Adaptation in
Semantic Segmentation,
WAD21(2829-2839)
IEEE DOI
2109
Training, Roads, Semantics, Employment, Training data,
Benchmark testing, Particle measurements
BibRef
Zhang, Y.[Youshan],
Davison, B.D.[Brian D.],
Deep Spherical Manifold Gaussian Kernel for Unsupervised Domain
Adaptation,
Diff-CVML21(4438-4447)
IEEE DOI
2109
Manifolds, Adaptation models,
Feature extraction, Pattern recognition, Noise measurement
BibRef
Jing, T.T.[Tao-Tao],
Ding, Z.M.[Zheng-Ming],
Adversarial Dual Distinct Classifiers for Unsupervised Domain
Adaptation,
WACV21(605-614)
IEEE DOI
2106
Visualization, Adaptation models,
Target recognition, Benchmark testing, Feature extraction
BibRef
Ringwald, T.[Tobias],
Stiefelhagen, R.[Rainer],
Adaptiope: A Modern Benchmark for Unsupervised Domain Adaptation,
WACV21(101-110)
IEEE DOI
2106
Systematics, Annotations, Training data, Benchmark testing, Cleaning
BibRef
Azzam, M.[Mohamed],
Gnanha, A.T.[Aurele Tohokantche],
Wong, H.S.[Hau-San],
Wu, S.[Si],
Adversarially Constrained Interpolation for Unsupervised Domain
Adaptation,
ICPR21(2375-2381)
IEEE DOI
2105
Manifolds, Training, Interpolation, Adaptation models,
Smoothing methods, Data models, Pattern recognition
BibRef
Tran, H.H.[Hai H.],
Ahn, S.[Sumyeong],
Lee, T.[Taeyoung],
Yi, Y.[Yung],
Enlarging Discriminative Power by Adding an Extra Class in
Unsupervised Domain Adaptation,
ICPR21(1812-1819)
IEEE DOI
2105
Training, Adaptation models, Clustering algorithms,
Predictive models, Feature extraction, Data models, Data mining
BibRef
Liu, X.F.[Xiao-Feng],
Hu, B.[Bo],
Liu, X.[Xiongchang],
Lu, J.[Jun],
You, J.[Jane],
Kong, L.[Lingsheng],
Energy-constrained Self-training for Unsupervised Domain Adaptation,
ICPR21(7515-7520)
IEEE DOI
2105
Training, Adaptation models, Image segmentation, Semantics,
Supervised learning, Minimization, Pattern recognition
BibRef
Xiao, R.X.[Rui-Xin],
Liu, Z.L.[Zhi-Lei],
Wu, B.Y.[Bao-Yuan],
Teacher-Student Competition for Unsupervised Domain Adaptation,
ICPR21(8291-8298)
IEEE DOI
2105
Training, Adaptation models, Benchmark testing, Feature extraction,
Pattern recognition
BibRef
Dai, S.Y.[Shu-Yang],
Cheng, Y.[Yu],
Zhang, Y.Z.[Yi-Zhe],
Gan, Z.[Zhe],
Liu, J.J.[Jing-Jing],
Carin, L.[Lawrence],
Contrastively Smoothed Class Alignment for Unsupervised Domain
Adaptation,
ACCV20(IV:268-283).
Springer DOI
2103
BibRef
Zhang, Y.S.[You-Shan],
Ye, H.[Hui],
Davison, B.D.[Brian D.],
Adversarial Reinforcement Learning for Unsupervised Domain Adaptation,
WACV21(635-644)
IEEE DOI
2106
BibRef
Earlier: A1, A3, Only:
Adversarial Continuous Learning in Unsupervised Domain Adaptation,
DLPR20(672-687).
Springer DOI
2103
Adaptation models,
Computational modeling, Neural networks, Reinforcement learning,
Feature extraction.
BibRef
Cicek, S.[Safa],
Xu, N.[Ning],
Wang, Z.W.[Zhao-Wen],
Jin, H.L.[Hai-Lin],
Soatto, S.[Stefano],
Spatial Class Distribution Shift in Unsupervised Domain Adaptation:
Local Alignment Comes to Rescue,
ACCV20(III:623-638).
Springer DOI
2103
BibRef
Li, C.C.[Cong-Cong],
Du, D.W.[Da-Wei],
Zhang, L.[Libo],
Wen, L.Y.[Long-Yin],
Luo, T.J.[Tie-Jian],
Wu, Y.J.[Yan-Jun],
Zhu, P.F.[Peng-Fei],
Spatial Attention Pyramid Network for Unsupervised Domain Adaptation,
ECCV20(XIII:481-497).
Springer DOI
2011
BibRef
Lee, J.,
Lee, G.,
Model Uncertainty for Unsupervised Domain Adaptation,
ICIP20(1841-1845)
IEEE DOI
2011
Uncertainty, Adaptation models, Feature extraction, Task analysis,
Bayes methods, Mathematical model, Monte Carlo methods,
image classification
BibRef
Mei, K.[Ke],
Zhu, C.[Chuang],
Zou, J.Q.[Jia-Qi],
Zhang, S.H.[Shang-Hang],
Instance Adaptive Self-training for Unsupervised Domain Adaptation,
ECCV20(XXVI:415-430).
Springer DOI
2011
BibRef
Peng, X.C.[Xing-Chao],
Li, Y.C.[Yi-Chen],
Saenko, K.[Kate],
Domain2vec: Domain Embedding for Unsupervised Domain Adaptation,
ECCV20(VI:756-774).
Springer DOI
2011
BibRef
Dong, J.H.[Jia-Hua],
Cong, Y.[Yang],
Sun, G.[Gan],
Liu, Y.Y.[Yu-Yang],
Xu, X.W.[Xiao-Wei],
CSCL: Critical Semantic-consistent Learning for Unsupervised Domain
Adaptation,
ECCV20(VIII:745-762).
Springer DOI
2011
BibRef
Zhang, Y.B.[Ya-Bin],
Deng, B.[Bin],
Jia, K.[Kui],
Zhang, L.[Lei],
Label Propagation with Augmented Anchors: A Simple Semi-supervised
Learning Baseline for Unsupervised Domain Adaptation,
ECCV20(IV:781-797).
Springer DOI
2011
BibRef
Li, M.,
Zhai, Y.,
Luo, Y.,
Ge, P.,
Ren, C.,
Enhanced Transport Distance for Unsupervised Domain Adaptation,
CVPR20(13933-13941)
IEEE DOI
2008
Feature extraction, Training, Measurement, Adaptation models,
Task analysis, Image reconstruction, Neural networks
BibRef
Ye, S.,
Wu, K.,
Zhou, M.,
Yang, Y.,
Tan, S.H.,
Xu, K.,
Song, J.,
Bao, C.,
Ma, K.,
Light-weight Calibrator: A Separable Component for Unsupervised
Domain Adaptation,
CVPR20(13733-13742)
IEEE DOI
2008
Adaptation models, Training, Feature extraction, Neural networks,
Data models, Performance evaluation
BibRef
Lu, Z.,
Yang, Y.,
Zhu, X.,
Liu, C.,
Song, Y.,
Xiang, T.,
Stochastic Classifiers for Unsupervised Domain Adaptation,
CVPR20(9108-9117)
IEEE DOI
2008
Training, Stochastic processes, Task analysis, Semantics,
Neural networks, Data models, Adaptation models
BibRef
Xu, R.,
Liu, P.,
Wang, L.,
Chen, C.,
Wang, J.,
Reliable Weighted Optimal Transport for Unsupervised Domain
Adaptation,
CVPR20(4393-4402)
IEEE DOI
2008
Reliability, Kernel, Training, Generators, Task analysis,
Measurement uncertainty
BibRef
Hu, L.,
Kan, M.,
Shan, S.,
Chen, X.,
Unsupervised Domain Adaptation With Hierarchical Gradient
Synchronization,
CVPR20(4042-4051)
IEEE DOI
2008
Feature extraction, Synchronization, Entropy, Task analysis,
Training, Adaptation models
BibRef
Cohen, T.[Tomer],
Wolf, L.B.[Lior B.],
Bidirectional One-Shot Unsupervised Domain Mapping,
ICCV19(1784-1792)
IEEE DOI
2004
Code, Learning.
WWW Link. image processing, unsupervised learning, single sample domain,
bidirectional one-shot unsupervised domain mapping,
Unsupervised learning
BibRef
Lee, S.,
Kim, D.,
Kim, N.,
Jeong, S.,
Drop to Adapt: Learning Discriminative Features for Unsupervised
Domain Adaptation,
ICCV19(91-100)
IEEE DOI
2004
Code, Domain Adaption.
WWW Link. feature extraction, image classification, image representation,
image segmentation, unsupervised learning, Neurons
BibRef
Hou, J.,
Ding, X.,
Deng, J.D.,
Cranefield, S.,
Unsupervised Domain Adaptation using Deep Networks with Cross-Grafted
Stacks,
TASKCV19(3257-3264)
IEEE DOI
2004
feature extraction, image representation,
learning (artificial intelligence), neural nets,
Cross grafted Stacks
BibRef
Gholami, B.,
Sahu, P.,
Kim, M.,
Pavlovic, V.,
Task-Discriminative Domain Alignment for Unsupervised Domain
Adaptation,
MDALC19(1327-1336)
IEEE DOI
2004
data structures, pattern clustering, unsupervised learning,
data structure, unsupervised domain adaptation,
Stochastic embedding
BibRef
Deng, Z.,
Luo, Y.,
Zhu, J.,
Cluster Alignment With a Teacher for Unsupervised Domain Adaptation,
ICCV19(9943-9952)
IEEE DOI
2004
pattern classification, pattern clustering,
unsupervised learning, cluster alignment, labeled source domain, Labeling
BibRef
Kim, S.,
Choi, J.,
Kim, T.,
Kim, C.,
Self-Training and Adversarial Background Regularization for
Unsupervised Domain Adaptive One-Stage Object Detection,
ICCV19(6091-6100)
IEEE DOI
2004
feature extraction, object detection, unsupervised learning, BSR,
target backgrounds, domain shift, foregrounds,
Semantics
BibRef
Xu, R.,
Li, G.,
Yang, J.,
Lin, L.,
Larger Norm More Transferable: An Adaptive Feature Norm Approach for
Unsupervised Domain Adaptation,
ICCV19(1426-1435)
IEEE DOI
2004
Code, Domain Adaption.
WWW Link. learning (artificial intelligence), task-specific features,
standard domain adaptation, partial domain adaptation,
Neural networks
BibRef
Binkowski, M.,
Hjelm, D.,
Courville, A.,
Batch Weight for Domain Adaptation With Mass Shift,
ICCV19(1844-1853)
IEEE DOI
2004
Bayes methods, language translation, probability,
unsupervised learning, transfer networks, Task analysis
BibRef
Kim, M.Y.[Min-Young],
Sahu, P.[Pritish],
Gholami, B.[Behnam],
Pavlovic, V.[Vladimir],
Unsupervised Visual Domain Adaptation:
A Deep Max-Margin Gaussian Process Approach,
CVPR19(4375-4385).
IEEE DOI
2002
BibRef
Chen, C.Q.[Chao-Qi],
Xie, W.P.[Wei-Ping],
Huang, W.B.[Wen-Bing],
Rong, Y.[Yu],
Ding, X.H.[Xing-Hao],
Huang, Y.[Yue],
Xu, T.Y.[Ting-Yang],
Huang, J.Z.[Jun-Zhou],
Progressive Feature Alignment for Unsupervised Domain Adaptation,
CVPR19(627-636).
IEEE DOI
2002
BibRef
Pan, Y.W.[Ying-Wei],
Yao, T.[Ting],
Li, Y.[Yehao],
Wang, Y.[Yu],
Ngo, C.W.[Chong-Wah],
Mei, T.[Tao],
Transferrable Prototypical Networks for Unsupervised Domain Adaptation,
CVPR19(2234-2242).
IEEE DOI
2002
BibRef
Lee, C.Y.[Chen-Yu],
Batra, T.[Tanmay],
Baig, M.H.[Mohammad Haris],
Ulbricht, D.[Daniel],
Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation,
CVPR19(10277-10287).
IEEE DOI
2002
BibRef
Chang, W.G.[Woong-Gi],
You, T.[Tackgeun],
Seo, S.[Seonguk],
Kwak, S.[Suha],
Han, B.H.[Bo-Hyung],
Domain-Specific Batch Normalization for Unsupervised Domain Adaptation,
CVPR19(7346-7354).
IEEE DOI
2002
BibRef
Roy, S.[Subhankar],
Siarohin, A.[Aliaksandr],
Sangineto, E.[Enver],
Bulo, S.R.[Samuel Rota],
Sebe, N.[Nicu],
Ricci, E.[Elisa],
Unsupervised Domain Adaptation Using Feature-Whitening and Consensus
Loss,
CVPR19(9463-9472).
IEEE DOI
2002
BibRef
Roy, S.[Subhankar],
Siarohin, A.[Aliaksandr],
Sebe, N.[Nicu],
Unsupervised Domain Adaptation Using Full-Feature Whitening and
Colouring,
CIAP19(II:225-236).
Springer DOI
1909
BibRef
Jamal, A.[Arshad],
Namboodiri, V.P.[Vinay P.],
Deodhare, D.[Dipti],
Venkatesh, K.S.,
U-DADA: Unsupervised Deep Action Domain Adaptation,
ACCV18(III:444-459).
Springer DOI
1906
BibRef
Park, H.[Hyoungwoo],
Ju, M.J.[Min-Jeong],
Moon, S.K.[Sang-Keun],
Yoo, C.D.[Chang D.],
Unsupervised Domain Adaptation for Object Detection Using Distribution
Matching in Various Feature Level,
IWDW18(363-372).
Springer DOI
1905
BibRef
Saito, K.,
Watanabe, K.,
Ushiku, Y.,
Harada, T.,
Maximum Classifier Discrepancy for Unsupervised Domain Adaptation,
CVPR18(3723-3732)
IEEE DOI
1812
Generators, Task analysis, Training, Neural networks, Semantics,
Feature extraction, Learning systems
BibRef
Pinheiro, P.O.,
Unsupervised Domain Adaptation with Similarity Learning,
CVPR18(8004-8013)
IEEE DOI
1812
Prototypes, Adaptation models, Training, Pollution measurement, Standards
BibRef
Yang, Z.,
Chen, W.,
Wang, F.,
Xu, B.,
Unsupervised Domain Adaptation for Neural Machine Translation,
ICPR18(338-343)
IEEE DOI
1812
Training, Adaptation models, Generators, Data models, Task analysis,
Transforms, Feature extraction
BibRef
Gui, C.,
Hu, J.,
Unsupervised Domain Adaptation by regularizing Softmax Activation,
ICPR18(397-402)
IEEE DOI
1812
Standards, Training, Entropy, Bridges, Feature extraction,
Benchmark testing, Kernel
BibRef
Xiao, P.,
Du, B.,
Yun, S.,
Lit, X.,
Zhang, Y.,
Wu, J.,
Probabilistic Graph Embedding for Unsupervised Domain Adaptation,
ICPR18(1283-1288)
IEEE DOI
1812
graph theory, matrix algebra, pattern classification, probability,
unsupervised learning, unlabeled target domain data,
Computational modeling
BibRef
Das, D.,
Lee, C.S.G.[C. S. George],
Unsupervised Domain Adaptation Using Regularized Hyper-Graph Matching,
ICIP18(3758-3762)
IEEE DOI
1809
Principal component analysis, Optimization, Object recognition,
Cats, Dogs, Tensile stress, Indexes, Domain Adaptation,
Object Recognition
BibRef
Zhu, L.,
Zhang, X.,
Zhang, W.,
Huang, X.,
Guan, N.,
Luo, Z.,
Unsupervised domain adaptation with joint supervised sparse coding
and discriminative regularization term,
ICIP17(3066-3070)
IEEE DOI
1803
Encoding, Image reconstruction, Kernel, Learning systems,
Optimization, Sparse matrices, Task analysis, Transfer learning,
subspace learning
BibRef
Csurka, G.,
Baradel, F.,
Chidlovskii, B.,
Clinchant, S.,
Discrepancy-Based Networks for Unsupervised Domain Adaptation:
A Comparative Study,
TASKCV17(2630-2636)
IEEE DOI
1802
Adaptation models, Data models, Feature extraction, Kernel, Painting, Training
BibRef
Gholami, B.,
Rudovic, O.,
Pavlovic, V.,
PUnDA: Probabilistic Unsupervised Domain Adaptation for Knowledge
Transfer Across Visual Categories,
ICCV17(3601-3610)
IEEE DOI
1802
Bayes methods, image classification, unsupervised learning, PUnDA,
classifier discriminative power, domain disparity,
Visualization
BibRef
Aljundi, R.[Rahaf],
Tuytelaars, T.[Tinne],
Lightweight Unsupervised Domain Adaptation by Convolutional Filter
Reconstruction,
TASKCV16(III: 508-515).
Springer DOI
1611
BibRef
Csurka, G.[Gabriela],
Chidlowskii, B.[Boris],
Clinchant, S.[Stéphane],
Michel, S.[Sophia],
Unsupervised Domain Adaptation with Regularized Domain Instance
Denoising,
TASKCV16(III: 458-466).
Springer DOI
1611
BibRef
Khan, M.N.A.,
Heisterkamp, D.R.,
Adapting instance weights for unsupervised domain adaptation using
quadratic mutual information and subspace learning,
ICPR16(1560-1565)
IEEE DOI
1705
BibRef
And:
Domain adaptation by iterative improvement of soft-labeling and
maximization of non-parametric mutual information,
ICIP16(4458-4462)
IEEE DOI
1610
Adaptation models, Data models, Iterative methods, Kernel, Labeling,
Mutual information, Training.
BibRef
Xu, M.W.[Ming-Wei],
Wu, S.S.[Song-Song],
Jing, X.Y.[Xiao-Yuan],
Yang, J.Y.[Jing-Yu],
Kernel subspace alignment for unsupervised domain adaptation,
ICIP15(2880-2884)
IEEE DOI
1512
domain adaptation; kernel subspace alignment; object recognition
BibRef
Caseiro, R.[Rui],
Henriques, J.F.[Joao F.],
Martins, P.[Pedro],
Batista, J.P.[Jorge P.],
Beyond the shortest path: Unsupervised domain adaptation by Sampling
Subspaces along the Spline Flow,
CVPR15(3846-3854)
IEEE DOI
1510
BibRef
Long, M.S.[Ming-Sheng],
Wang, J.M.[Jian-Min],
Ding, G.G.[Gui-Guang],
Sun, J.G.[Jia-Guang],
Yu, P.S.[Philip S.],
Transfer Joint Matching for Unsupervised Domain Adaptation,
CVPR14(1410-1417)
IEEE DOI
1409
BibRef
Earlier:
Transfer Feature Learning with Joint Distribution Adaptation,
ICCV13(2200-2207)
IEEE DOI
1403
distribution matching.
Transfer learning; feature learning; joint distribution adaptation
BibRef
Mirrashed, F.[Fatemeh],
Morariu, V.I.[Vlad I.],
Davis, L.S.[Larry S.],
Sampling for unsupervised domain adaptive object detection,
ICIP13(3288-3292)
IEEE DOI
1402
Domain Adaptation;Object Detection;Semi-supervised Learning
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Domain Generalization .