14.1.4.3 Transfer Learning from Other Classes, Domain Adaptation

Huang, P.P.[Pi-Pei], Wang, G.[Gang], Qin, S.Y.[Shi-Yin],
A novel learning approach to multiple tasks based on boosting methodology,
PRL(31), No. 12, 1 September 2010, pp. 1693-1700.
Elsevier DOI 1008
Boosting; Multi-task learning; Inductive transfer learning; Multiple tasks; Text classification BibRef

Huang, P.P.[Pi-Pei], Wang, G.[Gang], Qin, S.Y.[Shi-Yin],
Boosting for transfer learning from multiple data sources,
PRL(33), No. 5, 1 April 2012, pp. 568-579.
Elsevier DOI 1202
Opinion mining; Sentimental classification; Boosting; Transfer learning; Transfer learning with multiple sources; Multiple source domains BibRef

Yang, C.Y.[Chun-Yu], Zhou, J.[Jie],
Non-stationary data sequence classification using online class priors estimation,
PR(41), No. 8, August 2008, pp. 2656-2664.
Elsevier DOI 0805
Concept drift; Online classification; EM BibRef

Zhang, Z.H.[Zhi-Hao], Zhou, J.[Jie],
Transfer estimation of evolving class priors in data stream classification,
PR(43), No. 9, September 2010, pp. 3151-3161.
Elsevier DOI 1006
Concept drift; Transfer learning; Prior estimation BibRef

Gao, X.B.[Xin-Bo], Wang, X.M.[Xiu-Mei], Li, X.L.[Xue-Long], Tao, D.C.[Da-Cheng],
Transfer latent variable model based on divergence analysis,
PR(44), No. 10-11, October-November 2011, pp. 2358-2366.
Elsevier DOI 1101
Dimensionality reduction; Latent variable model; Transfer learning; Bregman divergence BibRef

Pang, J., Huang, Q., Yan, S., Jiang, S., Qin, L.,
Transferring Boosted Detectors Towards Viewpoint and Scene Adaptiveness,
IP(20), No. 5, May 2011, pp. 1388-1400.
IEEE DOI 1104
Training set isn't exactly the same as test data. BibRef

di Carlo, S., Falasconi, M., Sanchez, E., Scionti, A., Squillero, G., Tonda, A.,
Increasing pattern recognition accuracy for chemical sensing by evolutionary based drift compensation,
PRL(32), No. 13, 1 October 2011, pp. 1594-1603.
Elsevier DOI 1109
Sensor drift; Evolutionary strategy; Classification systems BibRef

Hasanat, M.H.A.[Mozaherul Hoque Abul], Ramachandram, D.[Dhanesh], Mandava, R.[Rajeswari],
Bayesian belief network learning algorithms for modeling contextual relationships in natural imagery: a comparative study,
AIR(34), No. 4, December 2010, pp. 291-308.
WWW Link. 1208
BibRef

Abbasnejad, M.E.[M. Ehsan], Ramachandram, D.[Dhanesh], Mandava, R.[Rajeswari],
Optimizing Kernel Functions Using Transfer Learning from Unlabeled Data,
ICMV09(111-117).
IEEE DOI 0912
BibRef

Luo, Y.[Yong], Liu, T.L.[Tong-Liang], Tao, D.C.[Da-Cheng], Xu, C.[Chao],
Multiview Matrix Completion for Multilabel Image Classification,
IP(24), No. 8, August 2015, pp. 2355-2368.
IEEE DOI 1505
Kernel BibRef

Luo, Y.[Yong], Liu, T.L.[Tong-Liang], Tao, D.C.[Da-Cheng], Xu, C.[Chao],
Decomposition-Based Transfer Distance Metric Learning for Image Classification,
IP(23), No. 9, September 2014, pp. 3789-3801.
IEEE DOI 1410
eigenvalues and eigenfunctions BibRef

Xu, C., Liu, T.L.[Tong-Liang], Tao, D.C.[Da-Cheng], Xu, C.[Chao],
Local Rademacher Complexity for Multi-Label Learning,
IP(25), No. 3, March 2016, pp. 1495-1507.
IEEE DOI 1602
Complexity theory BibRef

Yu, J.[Jun], Rui, Y.[Yong], Tang, Y.Y.[Yuan Yan], Tao, D.C.[Da-Cheng],
High-Order Distance-Based Multiview Stochastic Learning in Image Classification,
Cyber(44), No. 12, December 2014, pp. 2431-2442.
IEEE DOI 1402
content-based retrieval BibRef

Chen, J.H.[Jian-Hui], Tang, L.[Lei], Liu, J.[Jun], Ye, J.P.[Jie-Ping],
A Convex Formulation for Learning a Shared Predictive Structure from Multiple Tasks,
PAMI(35), No. 5, May 2013, pp. 1025-1038.
IEEE DOI 1304
BibRef

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

Gonçalves, Jr., P.M.[Paulo Mauricio], Maior de Barros, R.S.[Roberto Souto],
RCD: A recurring concept drift framework,
PRL(34), No. 9, July 2013, pp. 1018-1025.
Elsevier DOI 1305
Data streams; Concept drift; Recurring contexts; On-line learning; Multivariate non-parametric statistical test BibRef

Salakhutdinov, R.[Ruslan], Tenenbaum, J.B.[Joshua B.], Torralba, A.B.[Antonio B.],
Learning with Hierarchical-Deep Models,
PAMI(35), No. 8, 2013, pp. 1958-1971.
IEEE DOI 1307
BibRef
Earlier: A1, A3, A2:
Learning to share visual appearance for multiclass object detection,
CVPR11(1481-1488).
IEEE DOI 1106
Bayesian methods; Deep networks. Rare objects "borrow" statistical info from related common objects. Learn hierarchy of objects. BibRef

Lin, D.[Di], An, X.[Xing], Zhang, J.[Jian],
Double-bootstrapping source data selection for instance-based transfer learning,
PRL(34), No. 11, 1 August 2013, pp. 1279-1285.
Elsevier DOI 1306
Transfer learning; Instance-based transfer learning; Source data selection; Bagging BibRef

Varga, R., Nedevschi, S.,
Label Transfer by Measuring Compactness,
IP(22), No. 12, 2013, pp. 4711-4723.
IEEE DOI 1312
feature extraction BibRef

Li, W.[Wen], Duan, L.X.[Li-Xin], Xu, D.[Dong], Tsang, I.W.H.[Ivor Wai-Hung],
Learning With Augmented Features for Supervised and Semi-Supervised Heterogeneous Domain Adaptation,
PAMI(36), No. 6, June 2014, pp. 1134-1148.
IEEE DOI 1406
BibRef
Earlier: A1, A2, A4, A3:
Batch mode Adaptive Multiple Instance Learning for computer vision tasks,
CVPR12(2368-2375).
IEEE DOI 1208
Convergence BibRef

Xu, X.X.[Xin-Xing], Li, W.[Wen], Xu, D.[Dong], Tsang, I.W.H.[Ivor Wai-Hung],
Co-Labeling for Multi-View Weakly Labeled Learning,
PAMI(38), No. 6, June 2016, pp. 1113-1125.
IEEE DOI 1605
Kernel. See also Image Classification With Densely Sampled Image Windows and Generalized Adaptive Multiple Kernel Learning. BibRef

Hoffman, J.[Judy], Rodner, E.[Erik], Donahue, J.[Jeff], Kulis, B.[Brian], Saenko, K.[Kate],
Asymmetric and Category Invariant Feature Transformations for Domain Adaptation,
IJCV(109), No. 1-2, August 2014, pp. 28-41.
Springer DOI 1407
BibRef

Tzeng, E., Hoffman, J.[Judy], Saenko, K.[Kate], Darrell, T.J.[Trevor J.],
Adversarial Discriminative Domain Adaptation,
CVPR17(2962-2971)
IEEE DOI 1711
BibRef
Earlier: A2, A4, A3, Only:
Continuous Manifold Based Adaptation for Evolving Visual Domains,
CVPR14(867-874)
IEEE DOI 1409
Adaptation models, Gallium nitride, Image reconstruction, Standards, Training, Visualization BibRef

Tzeng, E., Hoffman, J., Darrell, T.J., Saenko, K.,
Simultaneous Deep Transfer Across Domains and Tasks,
ICCV15(4068-4076)
IEEE DOI 1602
Adaptation models BibRef

Hoffman, J.[Judy], Kulis, B.[Brian], Darrell, T.J.[Trevor J.], Saenko, K.[Kate],
Discovering Latent Domains for Multisource Domain Adaptation,
ECCV12(II: 702-715).
Springer DOI 1210
BibRef

Kulis, B.[Brian], Saenko, K.[Kate], Darrell, T.J.[Trevor J.],
What you saw is not what you get: Domain adaptation using asymmetric kernel transforms,
CVPR11(1785-1792).
IEEE DOI 1106
Training is not adequate. Domain adaptation. BibRef

Saenko, K.[Kate], Kulis, B.[Brian], Fritz, M.[Mario], Darrell, T.J.[Trevor J.],
Adapting Visual Category Models to New Domains,
ECCV10(IV: 213-226).
Springer DOI 1009
BibRef

Donahue, J.[Jeff], Hoffman, J.[Judy], Rodner, E.[Erik], Saenko, K.[Kate], Darrell, T.J.[Trevor J.],
Semi-supervised Domain Adaptation with Instance Constraints,
CVPR13(668-675)
IEEE DOI 1309
domain adaptation; visual recognition BibRef

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

Shao, M.[Ming], Kit, D.[Dmitry], Fu, Y.[Yun],
Generalized Transfer Subspace Learning Through Low-Rank Constraint,
IJCV(109), No. 1-2, August 2014, pp. 74-93.
Springer DOI 1407
Using existing data for transfer to new domains. BibRef

Ding, Z.M.[Zheng-Ming], Fu, Y.[Yun],
Robust Transfer Metric Learning for Image Classification,
IP(26), No. 2, February 2017, pp. 660-670.
IEEE DOI 1702
computational complexity BibRef

Ding, Z.M.[Zheng-Ming], Fu, Y.[Yun],
Deep Domain Generalization With Structured Low-Rank Constraint,
IP(27), No. 1, January 2018, pp. 304-313.
IEEE DOI 1712
computer vision, learning (artificial intelligence), neural nets, common knowledge, computer vision field, consistent knowledge, low-rank constraint BibRef

Ding, Z.M.[Zheng-Ming], Shao, M.[Ming], Fu, Y.[Yun],
Low-Rank Embedded Ensemble Semantic Dictionary for Zero-Shot Learning,
CVPR17(6005-6013)
IEEE DOI 1711
BibRef
Earlier:
Deep Robust Encoder Through Locality Preserving Low-Rank Dictionary,
ECCV16(VI: 567-582).
Springer DOI 1611
Dictionaries, Gold, Information science, Machine learning, Semantics, Training, Visualization BibRef

Ding, Z.M.[Zheng-Ming], Shao, M.[Ming], Fu, Y.[Yun],
Missing Modality Transfer Learning via Latent Low-Rank Constraint,
IP(24), No. 11, November 2015, pp. 4322-4334.
IEEE DOI 1509
learning (artificial intelligence) 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

Zhang, Z.H.[Zhi-Hao], Zhou, J.[Jie],
Multi-task clustering via domain adaptation,
PR(45), No. 1, 2012, pp. 465-473.
Elsevier DOI 1410
Multi-task clustering 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

Li, Q.[Qing], Chen, X.[Xiaowu], Song, Y.[Yafei], Zhang, Y., Jin, X.[Xin], Zhao, Q.P.[Qin-Ping],
Geodesic Propagation for Semantic Labeling,
IP(23), No. 11, November 2014, pp. 4812-4825.
IEEE DOI 1410
BibRef
Earlier: A2, A1, A3, A5, A6, Only:
Supervised Geodesic Propagation for Semantic Label Transfer,
ECCV12(III: 553-565).
Springer DOI 1210
Image color analysis BibRef

Khamis, S.[Sameh], Lampert, C.H.[Christoph H.],
CoConut: Co-Classification with Output Space Regularization,
BMVC14(xx-yy).
HTML Version. 1410
Jointly classifying multiple, otherwise independent, data samples. 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

Zhang, X.,
Convex Discriminative Multitask Clustering,
PAMI(37), No. 1, January 2015, pp. 28-40.
IEEE DOI 1412
Bismuth BibRef

Fang, M.[Min], Guo, Y.[Yong], Zhang, X.S.[Xiao-Song], Li, X.[Xiao],
Multi-source transfer learning based on label shared subspace,
PRL(51), No. 1, 2015, pp. 101-106.
Elsevier DOI 1412
Transfer learning 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

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

Sun, C.[Chao], Bao, B.K.[Bing-Kun], Xu, C.S.[Chang-Sheng],
Knowing Verb From Object: Retagging With Transfer Learning on Verb-Object Concept Images,
MultMed(17), No. 10, October 2015, pp. 1747-1759.
IEEE DOI 1511
Bayes methods 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

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

Xu, Y., Fang, X., Wu, J., Li, X., Zhang, D.,
Discriminative Transfer Subspace Learning via Low-Rank and Sparse Representation,
IP(25), No. 2, February 2016, pp. 850-863.
IEEE DOI 1601
Adaptation models BibRef

Deng, C., Tang, X., Yan, J., Liu, W., Gao, X.,
Discriminative Dictionary Learning With Common Label Alignment for Cross-Modal Retrieval,
MultMed(18), No. 2, February 2016, pp. 208-218.
IEEE DOI 1601
Correlation BibRef

Mudunuri, S.P.[Sivaram Prasad], Biswas, S.[Soma],
A coupled discriminative dictionary and transformation learning approach with applications to cross domain matching,
PRL(71), No. 1, 2016, pp. 38-44.
Elsevier DOI 1602
Face recognition BibRef

Mandal, D., Biswas, S.[Soma],
Generalized Coupled Dictionary Learning Approach With Applications to Cross-Modal Matching,
IP(25), No. 8, August 2016, pp. 3826-3837.
IEEE DOI 1608
Correlation BibRef

Das, N., Mandal, D., Biswas, S.[Soma],
Simultaneous Semi-Coupled Dictionary Learning for Matching in Canonical Space,
IP(26), No. 8, August 2017, pp. 3995-4004.
IEEE DOI 1707
BibRef
Earlier:
Simultaneous Semi-Coupled Dictionary Learning for Matching RGBD Data,
Biometrics16(243-251)
IEEE DOI 1612
computer vision, image matching, image representation, Mahalanobis metric, canonical representation, canonical space, computer vision, cross-modal recognition, simultaneous semicoupled dictionary learning, sparse coefficients, Computer vision, Couplings, Dictionaries, Linear programming, Measurement, Testing, Training, Cross-modal matching, RGBD data, audio-image data, privileged information, text-image, data BibRef

An, T.H.[Taeg-Hyun], Hong, K.S.[Ki-Sang],
Label transfer via sparse representation,
PRL(70), No. 1, 2016, pp. 1-7.
Elsevier DOI 1602
Scene parsing BibRef

Zhang, L.[Lei], Zuo, W.M.[Wang-Meng], Zhang, D.,
LSDT: Latent Sparse Domain Transfer Learning for Visual Adaptation,
IP(25), No. 3, March 2016, pp. 1177-1191.
IEEE DOI 1602
Big Data 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

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., Crawford, M.M., Zhu, L., Liu, Y.,
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

Hou, C.A.[Cheng-An], Tsai, Y.H.H.[Yao-Hung Hubert], Yeh, Y.R.[Yi-Ren], Wang, Y.C.F.[Yu-Chiang 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.F.[Yu-Chiang 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.F.[Yu-Chiang 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.F.[Yu-Chiang 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

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

Zhou, S.[Shuang], Smirnov, E.[Evgueni], Schoenmakers, G.[Gijs], Driessens, K.[Kurt], Peeters, R.[Ralf],
Testing exchangeability for transfer decision,
PRL(88), No. 1, 2017, pp. 64-71.
Elsevier DOI 1703
Instance-transfer learning BibRef

Shoeleh, F.[Farzaneh], Asadpour, M.[Masoud],
Graph based skill acquisition and transfer Learning for continuous reinforcement learning domains,
PRL(87), No. 1, 2017, pp. 104-116.
Elsevier DOI 1703
Reinforcement learning BibRef

Wang, J.[Jie], Luo, C.[Chang], Huang, H.Q.[Han-Qiao], Zhao, H.Z.[Hui-Zhen], Wang, S.Q.[Shi-Qiang],
Transferring Pre-Trained Deep CNNs for Remote Scene Classification with General Features Learned from Linear PCA Network,
RS(9), No. 3, 2017, pp. xx-yy.
DOI Link 1704
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, Computer vision, 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

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

Ghifary, M.[Muhammad], Balduzzi, D.[David], Kleijn, W.B.[W. Bastiaan], Zhang, M.J.[Meng-Jie],
Scatter Component Analysis: A Unified Framework for Domain Adaptation and Domain Generalization,
PAMI(39), No. 7, July 2017, pp. 1414-1430.
IEEE DOI 1706
BibRef
Earlier: A1, A3, A2, A4:
Domain Generalization for Object Recognition with Multi-task Autoencoders,
ICCV15(2551-2559)
IEEE DOI 1602
Algorithm design and analysis, Kernel, Object recognition, Optimization, Standards, Training, Visualization, Domain adaptation, domain generalization, feature learning, kernel methods, object recognition, scatter BibRef

Ghifary, M.[Muhammad], Kleijn, W.B.[W. Bastiaan], Zhang, M.J.[Meng-Jie], Balduzzi, D.[David], Li, W.[Wen],
Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation,
ECCV16(IV: 597-613).
Springer DOI 1611
Feature extraction BibRef

Qi, G.J., Liu, W., Aggarwal, C., Huang, T.,
Joint Intermodal and Intramodal Label Transfers for Extremely Rare or Unseen Classes,
PAMI(39), No. 7, July 2017, pp. 1360-1373.
IEEE DOI 1706
Bridges, Noise measurement, Semantics, Training, Transfer functions, Videos, Visualization, Multimodal analysis, image classification, intermodal and intramodal label transfers (I2LT), zero-shot, learning 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

Othman, E., Bazi, Y., Melgani, F., Alhichri, H., Alajlan, N., Zuair, M.,
Domain Adaptation Network for Cross-Scene Classification,
GeoRS(55), No. 8, August 2017, pp. 4441-4456.
IEEE DOI 1708
Computer architecture, Earth, Feature extraction, Feeds, Machine learning, Neural networks, Remote sensing, Cross-scene classification, distribution mismatch, domain adaptation, multisensor data, pretrained, convolutional, neural, network, (CNN) 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

Li, P.[Ping], Chen, S.C.[Song-Can],
Hierarchical Gaussian Processes model for multi-task learning,
PR(74), No. 1, 2018, pp. 134-144.
Elsevier DOI 1711
GP-LVM BibRef

Li, P.[Ping], Chen, S.C.[Song-Can],
Gaussian process approach for metric learning,
PR(87), 2019, pp. 17-28.
Elsevier DOI 1812
Metric learning, Gaussian process, Bilinear similarity, Non-parametric metric BibRef

Venkateswara, H.[Hemanth], Chakraborty, S.[Shayok], Panchanathan, S.[Sethuraman],
Deep-Learning Systems for Domain Adaptation in Computer Vision: Learning Transferable Feature Representations,
SPMag(34), No. 6, November 2017, pp. 117-129.
IEEE DOI 1712
BibRef
Earlier:
Nonlinear Embedding Transform for Unsupervised Domain Adaptation,
TASKCV16(III: 451-457).
Springer DOI 1611
Adaptation models, Data models, Knowledge transfer, Machine learning, Training data 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, Computer vision, Entropy, Labeling, Machine learning, Training, Uncertainty, Computer vision, 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

Wang, J.[Jun], Wang, G.Q.[Guo-Qing], Li, L.[Leida],
Image Pattern Similarity Index and Its Application to Task-Specific Transfer Learning,
IEICE(E100-D), No. 12, December 2017, pp. 3032-3035.
WWW Link. 1712
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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

Gao, N.N.[Neng-Neng], Huang, S.J.[Sheng-Jun], Yan, Y.F.[Yi-Fan], Chen, S.C.[Song-Can],
Cross modal similarity learning with active queries,
PR(75), No. 1, 2018, pp. 214-222.
Elsevier DOI 1712
Active learning BibRef

Liu, A.[Anjin], Lu, J.[Jie], Liu, F.[Feng], Zhang, G.Q.[Guang-Quan],
Accumulating regional density dissimilarity for concept drift detection in data streams,
PR(76), No. 1, 2018, pp. 256-272.
Elsevier DOI 1801
Concept drift BibRef

Zhang, B.C.[Bao-Chang], Perina, A.[Alessandro], Li, C.[Ce], Ye, Q.X.[Qi-Xiang], Murino, V.[Vittorio], Del Bue, A.[Alessio],
Manifold constraint transfer for visual structure-driven optimization,
PR(77), 2018, pp. 87-98.
Elsevier DOI 1802
Manifold, Transfer learning, Alternating direction method of multipliers, Object tracking, Object categorization 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

Kumar, S.[Suren], Dhiman, V.[Vikas], Koch, P.A.[Parker A.], Corso, J.J.[Jason J.],
Learning Compositional Sparse Bimodal Models,
PAMI(40), No. 5, May 2018, pp. 1032-1044.
IEEE DOI 1804
Transfer from, e.g., red triangles/blue squares to red squares/blue triangles. Dictionaries, Encoding, Poles and towers, Robot sensing systems, Semantics, Visualization, Multimodal learning, tabletop robotics BibRef

Zheng, F.[Feng], Tang, Y.[Yi], Shao, L.[Ling],
Hetero-Manifold Regularisation for Cross-Modal Hashing,
PAMI(40), No. 5, May 2018, pp. 1059-1071.
IEEE DOI 1804
Data models, Euclidean distance, Hamming distance, Manifolds, Semantics, Support vector machines, Training, Cross-modal hashing, manifold regularisation BibRef

Zhu, F.[Fan], Shao, L.[Ling], Tang, J.[Jun],
Boosted Cross-Domain Categorization,
BMVC14(xx-yy).
HTML Version. 1410
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Li, W.[Wen], Xu, Z.[Zheng], Xu, D.[Dong], Dai, D.X.[Deng-Xin], Van Gool, L.J.[Luc J.],
Domain Generalization and Adaptation Using Low Rank Exemplar SVMs,
PAMI(40), No. 5, May 2018, pp. 1114-1127.
IEEE DOI 1804
Linear programming, Logistics, Support vector machines, Testing, Training, Videos, Visualization, Latent domains, domain adaptation, exemplar SVMs BibRef

Wang, Y., Li, W., Dai, D.X.[Deng-Xin], Van Gool, L.J.[Luc J.],
Deep Domain Adaptation by Geodesic Distance Minimization,
TASKCV17(2651-2657)
IEEE DOI 1802
Adaptation models, Covariance matrices, Euclidean distance, Feature extraction, Manifolds, Training data, Visualization BibRef

Chang, H.[Hang], Han, J.[Ju], Zhong, C.[Cheng], Snijders, A.M.[Antoine M.], Mao, J.H.[Jian-Hua],
Unsupervised Transfer Learning via Multi-Scale Convolutional Sparse Coding for Biomedical Applications,
PAMI(40), No. 5, May 2018, pp. 1182-1194.
IEEE DOI 1804
Biological neural networks, Convolutional codes, Encoding, Feature extraction, Knowledge engineering, Training, sharable information 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

Xu, W.[Wei], Liu, W.[Wei], Chi, H.[Haoyuan], Huang, X.L.[Xiao-Lin], Yang, J.[Jie],
Multi-task classification with sequential instances and tasks,
SP:IC(64), 2018, pp. 59-67.
Elsevier DOI 1804
Classification, Multi-task learning, Curriculum learning, Self-paced learning BibRef

Sarafianos, N.[Nikolaos], Giannakopoulos, T.[Theodoros], Nikou, C.[Christophoros], Kakadiaris, I.A.[Ioannis A.],
Curriculum learning of visual attribute clusters for multi-task classification,
PR(80), 2018, pp. 94-108.
Elsevier DOI 1805
BibRef
Earlier:
Curriculum Learning for Multi-task Classification of Visual Attributes,
TASKCV17(2608-2615)
IEEE DOI 1802
Curriculum learning, Multi-task classification. Visual attribute classification. Learn individual groups of tasks. Correlation, Feature extraction, Machine learning, Training, Visualization 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

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.[Ruixi], 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

Xu, Z.[Zhi], Cai, G.Y.[Guo-Yong], Wen, Y.M.[Yi-Min], Chen, D.D.[Dong-Dong], Han, L.Y.[Li-Yao],
Image set-based classification using collaborative exemplars representation,
SIViP(12), No. 4, May 2018, pp. 607-615.
Springer DOI 1805
Represent the image sets and deal with outliers. BibRef

Peng, P.X.[Pei-Xi], Tian, Y.H.[Yong-Hong], Xiang, T.[Tao], Wang, Y.W.[Yao-Wei], Pontil, M.[Massimiliano], Huang, T.J.[Tie-Jun],
Joint Semantic and Latent Attribute Modelling for Cross-Class Transfer Learning,
PAMI(40), No. 7, July 2018, pp. 1625-1638.
IEEE DOI 1806
Adaptation models, Computational modeling, Data models, Dictionaries, Semantics, Training, Visualization, Attribute learning, zero-shot learning BibRef

Jiang, W.H.[Wen-Hao], Liu, W.[Wei], Chung, F.L.[Fu-Lai],
Knowledge transfer for spectral clustering,
PR(81), 2018, pp. 484-496.
Elsevier DOI 1806
Transfer learning, Spectral clustering, Co-clustering, Multi-task learning BibRef

Guo, X.[Xin], Wang, S.[Song], Tie, Y.[Yun], Qi, L.[Lin], Guan, L.[Ling],
Joint intermodal and intramodal correlation preservation for semi-paired learning,
PR(81), 2018, pp. 36-49.
Elsevier DOI 1806
Semi-paired learning, Canonical correlation analysis, Clustering 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

Shi, Q., Liu, X., Huang, X.,
An Active Relearning Framework for Remote Sensing Image Classification,
GeoRS(56), No. 6, June 2018, pp. 3468-3486.
IEEE DOI 1806
Adaptation models, Convergence, Feature extraction, Remote sensing, Spectral analysis, Training, Uncertainty, Active learning (AL), relearning 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

Chen, Y.[Yu], Yang, C.[Chunling], 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

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

Han, W.[Wei], Feng, R.[Ruyi], Wang, L.Z.[Li-Zhe], Cheng, Y.F.[Ya-Fan],
A semi-supervised generative framework with deep learning features for high-resolution remote sensing image scene classification,
PandRS(145), 2018, pp. 23-43.
Elsevier DOI 1810
Scene classification, Deep learning, Self-label, High resolution remote sensing images 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

Ding, Z., Fu, Y.,
Dual Low-Rank Decompositions for Robust Cross-View Learning,
IP(28), No. 1, January 2019, pp. 194-204.
IEEE DOI 1810
Manifolds, Robustness, Feature extraction, Face, Task analysis, Data models, Sensors, Cross-view learning, low-rank modeling, graph embedding BibRef

Cao, W.M.[Wen-Ming], Qian, S.[Sheng], Wu, S.[Si], Wong, H.S.[Hau-San],
Unsupervised Multi-task Learning with Hierarchical Data Structure,
PR(86), 2019, pp. 248-264.
Elsevier DOI 1811
Multi-task learning, hierarchical structure, unsupervised learning, structural similarity, BibRef

Ma, Z.C.[Zhong-Chen], Chen, S.C.[Song-Can], Ma, D.[Di],
Heterogeneous multi-output classification by structured conditional risk minimization,
PRL(116), 2018, pp. 50-57.
Elsevier DOI 1812
Multi-output classification, Problem transformation, Output structures, Heterogeneity, Structured prediction BibRef

Mejjati, Y.A., Cosker, D., Kim, K.I.,
Multi-task Learning by Maximizing Statistical Dependence,
CVPR18(3465-3473)
IEEE DOI 1812
Task analysis, Random variables, Kernel, Mutual information, Gaussian processes, Probability distribution, Classification algorithms BibRef

Liu, C.[Cheng], Zheng, C.T.[Chu-Tao], Qian, S.[Sheng], Wu, S.[Si], Wong, H.S.[Hau-San],
Encoding sparse and competitive structures among tasks in multi-task learning,
PR(88), 2019, pp. 689-701.
Elsevier DOI 1901
Multi-task learning, Sparse exclusive lasso, Task-competitive BibRef

Duan, L.J.[Li-Juan], En, Q.[Qing], Qiao, Y.H.[Yuan-Hua], Cui, S.[Song], Qing, L.Y.[Lai-Yun],
Deep feature representation based on privileged knowledge transfer,
PRL(119), 2019, pp. 62-70.
Elsevier DOI 1902
Feature representation, Privileged knowledge transfer, Deep neural network, Multi-object retrieval BibRef

Shi, Q.[Qian], Zhang, Y.[Yipeng], Liu, X.P.[Xiao-Ping], Zhao, K.[Kefei],
Regularised transfer learning for hyperspectral image classification,
IET-CV(13), No. 2, March 2019, pp. 188-193.
DOI Link 1902
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Tang, X.Y.[Xin-Yao], Du, B.[Bo], Huang, J.Z.[Jian-Zhong], Wang, Z.[Zengmao], Zhang, L.F.[Le-Fei],
On combining active and transfer learning for medical data classification,
IET-CV(13), No. 2, March 2019, pp. 194-205.
DOI Link 1902
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Rozantsev, A., Salzmann, M., Fua, P.,
Beyond Sharing Weights for Deep Domain Adaptation,
PAMI(41), No. 4, April 2019, pp. 801-814.
IEEE DOI 1903
BibRef
Earlier:
Residual Parameter Transfer for Deep Domain Adaptation,
CVPR18(4339-4348)
IEEE DOI 1812
Training, Machine learning, Task analysis, Computer architecture, Computer vision, Training data, Detectors, Domain adaptation, deep learning. Transforms, Streaming media, Complexity theory, Feature extraction, Adaptation models. BibRef

Luo, Y.[Yong], Wen, Y.G.[Yong-Gang], Liu, T.L.[Tong-Liang], Tao, D.C.[Da-Cheng],
Transferring Knowledge Fragments for Learning Distance Metric from a Heterogeneous Domain,
PAMI(41), No. 4, April 2019, pp. 1013-1026.
IEEE DOI 1903
Measurement, Task analysis, Face, Visualization, Feature extraction, Training, Pattern recognition, Transfer learning, nonlinear BibRef

Deng, C.[Cheng], Xue, Y.M.[Yu-Meng], Liu, X.L.[Xiang-Long], Li, C.[Chao], Tao, D.C.[Da-Cheng],
Active Transfer Learning Network: A Unified Deep Joint Spectral-Spatial Feature Learning Model for Hyperspectral Image Classification,
GeoRS(57), No. 3, March 2019, pp. 1741-1754.
IEEE DOI 1903
feature extraction, geophysical image processing, image classification, learning (artificial intelligence), transfer learning (TL) BibRef

Ji, R.R.[Rong-Rong], Gao, Y.[Yue], Hong, R.C.[Ri-Chang], Liu, Q.[Qiong], Tao, D.C.[Da-Cheng], Li, X.L.[Xue-Long],
Spectral-Spatial Constraint Hyperspectral Image Classification,
GeoRS(52), No. 3, March 2014, pp. 1811-1824.
IEEE DOI 1403
hyperspectral imaging BibRef

Luo, F.[Fulin], Du, B.[Bo], Zhang, L.P.[Liang-Pei], Zhang, L.F.[Le-Fei], Tao, D.C.[Da-Cheng],
Feature Learning Using Spatial-Spectral Hypergraph Discriminant Analysis for Hyperspectral Image,
Cyber(49), No. 7, July 2019, pp. 2406-2419.
IEEE DOI 1905
Feature extraction, Laplace equations, Learning systems, Cybernetics, Hyperspectral imaging, Image reconstruction, spatial-spectral information BibRef

Basavaraju, S.[Sathisha], Gaj, S.[Sibaji], Sur, A.[Arijit],
Object Memorability Prediction using Deep Learning: Location and Size Bias,
JVCIR(59), 2019, pp. 117-127.
Elsevier DOI 1903
Object Memorability, Deep Learning, Transfer Learning 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
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Ye, R.[Rui], Dai, Q.[Qun], Li, M.[MeiLing],
A hybrid transfer learning algorithm incorporating TrSVM with GASEN,
PR(92), 2019, pp. 192-202.
Elsevier DOI 1905
Transfer learning, Ensemble selection, Genetic Algorithm based Selective Ensemble algorithm (GASEN), Transfer Learning algorithm incorporating TrSVM with GASEN (TrGASVM) 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

Wang, C.[Chao], Tuo, H.[Hongya], Wang, J.X.[Jie-Xin], Qiao, L.F.[Ling-Feng],
Discriminative transfer learning via local and global structure preservation,
SIViP(13), No. 4, June 2019, pp. 753-760.
Springer DOI 1906
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Zhang, J.[Jing], Li, W.Q.[Wan-Qing], Ogunbona, P.[Philip], Xu, D.[Dong],
Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective,
Surveys(51), No. 1, February 2019, pp. Article No 7.
DOI Link 1906
Survey, Transfer Learning. BibRef

Xue, S.[Shan], Lu, J.[Jie], Zhang, G.Q.[Guang-Quan],
Cross-domain network representations,
PR(94), 2019, pp. 135-148.
Elsevier DOI 1906
Network representation, Transfer learning, Random walk, Information network, Unsupervised learning, Feature learning BibRef

Yu, X.[Xu], Jiang, F.[Feng], Du, J.[Junwei], Gong, D.[Dunwei],
A cross-domain collaborative filtering algorithm with expanding user and item features via the latent factor space of auxiliary domains,
PR(94), 2019, pp. 96-109.
Elsevier DOI 1906
Cross-domain collaborative filtering, Feature expansion, Funk-SVD decomposition, Classification, Latent factor space BibRef

Rostami, M.[Mohammad], Kolouri, S.[Soheil], Eaton, E.[Eric], Kim, K.[Kyungnam],
Deep Transfer Learning for Few-Shot SAR Image Classification,
RS(11), No. 11, 2019, pp. xx-yy.
DOI Link 1906
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Hong, D., Yokoya, N., Chanussot, J., Zhu, X.X.,
CoSpace: Common Subspace Learning From Hyperspectral-Multispectral Correspondences,
GeoRS(57), No. 7, July 2019, pp. 4349-4359.
IEEE DOI 1907
Hyperspectral imaging, Optimization, Satellites, Earth, Manifolds, Common subspace learning (CoSpace), cross-modality learning, remote sensing BibRef

Chen, W., Hsu, T.H., Tsai, Y.H., Chen, M., Wang, Y.F.,
Transfer Neural Trees: Semi-Supervised Heterogeneous Domain Adaptation and Beyond,
IP(28), No. 9, Sep. 2019, pp. 4620-4633.
IEEE DOI 1908
image classification, learning (artificial intelligence), pattern classification, trees (mathematics), zero-shot learning 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

Zhu, R.[Ruixi], Yan, L.[Li], Mo, N.[Nan], Liu, Y.[Yi],
Semi-supervised center-based discriminative adversarial learning for cross-domain scene-level land-cover classification of aerial images,
PandRS(155), 2019, pp. 72-89.
Elsevier DOI 1908
Semi-supervised domain adaptation, Scene-level land-cover classification, Triplet network, Center loss 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

Ma, X., Zhang, T., Xu, C.,
Deep Multi-Modality Adversarial Networks for Unsupervised Domain Adaptation,
MultMed(21), No. 9, September 2019, pp. 2419-2431.
IEEE DOI 1909
Feature extraction, Task analysis, Semantics, Training, Adaptation models, Correlation, Data mining, social event recognition 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

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

Tan, M., Yu, J., Zhang, H., Rui, Y., Tao, D.,
Image Recognition by Predicted User Click Feature With Multidomain Multitask Transfer Deep Network,
IP(28), No. 12, December 2019, pp. 6047-6062.
IEEE DOI 1909
Image recognition, Task analysis, Visualization, Predictive models, Adaptation models, Correlation, Computational modeling, word embedding 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., 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

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
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Javed, K.[Khurram], Shafait, F.[Faisal],
Revisiting Distillation and Incremental Classifier Learning,
ACCV18(VI:3-17).
Springer DOI 1906
Learn tasks incrementally BibRef

Zhu, Y.[Yi], Xue, J.[Jia], Newsam, S.[Shawn],
Gated Transfer Network for Transfer Learning,
ACCV18(IV:354-369).
Springer DOI 1906
BibRef

Kim, J.[Jaekyum], Koh, J.[Junho], Kim, Y.[Yecheol], Choi, J.[Jaehyung], Hwang, Y.[Youngbae], Choi, J.W.[Jun Won],
Robust Deep Multi-modal Learning Based on Gated Information Fusion Network,
ACCV18(IV:90-106).
Springer DOI 1906
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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

Zhong, Y.[Yang], Li, V.[Vladimir], Okada, R.[Ryuzo], Maki, A.[Atsuto],
Target Aware Network Adaptation for Efficient Representation Learning,
CEFR-LCV18(IV:450-467).
Springer DOI 1905
Finding the CNN that works. 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
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Mancini, M.[Massimiliano], Ricci, E.[Elisa], Caputo, B.[Barbara], Bulò, S.R.[Samuel Rota],
Adding New Tasks to a Single Network with Weight Transformations Using Binary Masks,
TASKCV18(II:180-189).
Springer DOI 1905
BibRef

D'Innocente, A.[Antonio], Caputo, B.[Barbara],
Domain Generalization with Domain-Specific Aggregation Modules,
GCPR18(187-198).
Springer DOI 1905
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Okatani, T.[Takayuki], Liu, X.[Xing], Suganuma, M.[Masanori],
Improving Generalization Ability of Deep Neural Networks for Visual Recognition Tasks,
CCIW19(3-13).
Springer DOI 1905
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Sawatzky, J.[Johann], Garbade, M.[Martin], Gall, J.[Juergen],
Ex Paucis Plura: Learning Affordance Segmentation from Very Few Examples,
GCPR18(169-184).
Springer DOI 1905
Use existing annotated datasets to annotate new ones (reduce what needs more work). BibRef

Sayed, N.[Nawid], Brattoli, B.[Biagio], Ommer, B.[Björn],
Cross and Learn: Cross-Modal Self-supervision,
GCPR18(228-243).
Springer DOI 1905
BibRef

Romijnders, R.[Rob], Meletis, P., Dubbelman, G.,
A Domain Agnostic Normalization Layer for Unsupervised Adversarial Domain Adaptation,
WACV19(1866-1875)
IEEE DOI 1904
image segmentation, semantic networks, unsupervised learning, unsupervised adversarial domain adaptation, Biological neural networks BibRef

Rahman, M.M.[Mohammad Mahfujur], Fookes, C.[Clinton], Baktashmotlagh, M.[Mahsa], Sridharan, S.[Sridha],
Multi-Component Image Translation for Deep Domain Generalization,
WACV19(579-588)
IEEE DOI 1904
image classification, learning (artificial intelligence), neural nets, protocols, protocol, synthetic images, Adaptation models BibRef

Chen, D., Liang, X., Wang, Y., Gao, W.,
Soft Transfer Learning via Gradient Diagnosis for Visual Relationship Detection,
WACV19(1118-1126)
IEEE DOI 1904
inference mechanisms, learning (artificial intelligence), object detection, optimisation, diverse language ambiguities, Dogs BibRef

Wang, T., Huan, J., Zhu, M.,
Instance-Based Deep Transfer Learning,
WACV19(367-375)
IEEE DOI 1904
computer vision, image classification, learning (artificial intelligence), Load modeling BibRef

Li, J.S.[Jian-Shu], Zhou, P.[Pan], Chen, Y.P.[Yun-Peng], Zhao, J.[Jian], Roy, S.[Sujoy], Yan, S.C.[Shui-Cheng], Feng, J.S.[Jia-Shi], Sim, T.[Terence],
Task Relation Networks,
WACV19(932-940)
IEEE DOI 1904
covariance matrices, learning (artificial intelligence), task analysis, task covariance modeling, Heuristic algorithms See also Estimation of Affective Level in the Wild with Multiple Memory Networks. BibRef

Yi, H.Y.[Hai-Yang], Xu, Z.[Zhi], Wen, Y.M.[Yi-Min], Fan, Z.G.[Zhi-Gang],
Multi-source Domain Adaptation for Face Recognition,
ICPR18(1349-1354)
IEEE DOI 1812
Face recognition, Image reconstruction, Correlation, Optimization, Feature extraction, Sparse matrices, Training, domain adaptation, face recognition BibRef

Liu, Y.C.[Yen-Cheng], Yeh, Y.Y.[Yu-Ying], Fu, T.C.[Tzu-Chien], Wang, S.D.[Sheng-De], Chiu, W.C.[Wei-Chen], Wang, Y.C.F.[Yu-Chiang 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

Mallya, A.[Arun], Lazebnik, S.[Svetlana],
PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning,
CVPR18(7765-7773)
IEEE DOI 1812
Task analysis, Training, Computer architecture, Neural networks, Network architecture, Robustness, Training data BibRef

Zhang, Y.[Ying], Xiang, T.[Tao], Hospedales, T.M.[Timothy M.], Lu, H.C.[Hu-Chuan],
Deep Mutual Learning,
CVPR18(4320-4328)
IEEE DOI 1812
Training, Computational modeling, Task analysis, Supervised learning, Optimization, Entropy, Neural networks BibRef

Kundu, J.N.[Jogendra Nath], Uppala, P.K.[Phani Krishna], Pahuja, A.[Anuj], Babu, R.V.[R. Venkatesh],
AdaDepth: Unsupervised Content Congruent Adaptation for Depth Estimation,
CVPR18(2656-2665)
IEEE DOI 1812
Task analysis, Estimation, Semantics, Adaptation models, Training, Predictive models. 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

Zhang, Z., Wang, M., Huang, Y., Nehorai, A.,
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

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

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

Mancini, M.[Massimiliano], Porzi, L.[Lorenzo], Bulò, S.R., Caputo, B.[Barbara], Ricci, E.,
Boosting Domain Adaptation by Discovering Latent Domains,
CVPR18(3771-3780)
IEEE DOI 1812
Data models, Adaptation models, Neural networks, Computer vision, Computer architecture, Training data, Training BibRef

Xu, R., Chen, Z., Zuo, W., Yan, J., Lin, L.,
Deep Cocktail Network: Multi-source Unsupervised Domain Adaptation with Category Shift,
CVPR18(3964-3973)
IEEE DOI 1812
Feature extraction, Adaptation models, Training, Protocols, Task analysis, Benchmark testing, Visualization 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

Lee, K., He, X., Zhang, L., Yang, L.,
CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise,
CVPR18(5447-5456)
IEEE DOI 1812
Noise measurement, Feature extraction, Training, Task analysis, Neural networks, Labeling, Manuals BibRef

Zamir, A.R., Sax, A., Shen, W., Guibas, L., Malik, J., Savarese, S.,
Taskonomy: Disentangling Task Transfer Learning,
CVPR18(3712-3722)
IEEE DOI 1812
Task analysis, Computational modeling, Taxonomy, Dictionaries BibRef

Chen, S., Zhang, C., Dong, M.,
Coupled End-to-End Transfer Learning with Generalized Fisher Information,
CVPR18(4329-4338)
IEEE DOI 1812
Task analysis, Training, Decoding, Neural networks, Adaptation models, Image reconstruction, Feature extraction BibRef

Huang, X., Peng, Y.,
Deep Cross-Media Knowledge Transfer,
CVPR18(8837-8846)
IEEE DOI 1812
Media, Training, Correlation, Semantics, Adaptation models, Knowledge transfer, Computational modeling BibRef

Noroozi, M., Vinjimoor, A., Favaro, P., Pirsiavash, H.,
Boosting Self-Supervised Learning via Knowledge Transfer,
CVPR18(9359-9367)
IEEE DOI 1812
Task analysis, Computer architecture, Computational modeling, Training, Feature extraction, Data models, Measurement BibRef

Mormont, R., Geurts, P., Marée, R.,
Comparison of Deep Transfer Learning Strategies for Digital Pathology,
Microscopy18(2343-234309)
IEEE DOI 1812
Feature extraction, Task analysis, Biomedical imaging, Training, Support vector machines, Computer vision BibRef

Lu, W., Chung, F.,
A Deep Graphical Model for Layered Knowledge Transfer,
ICPR18(260-265)
IEEE DOI 1812
Task analysis, Dictionaries, Adaptation models, Inference algorithms, Graphical models, Training BibRef

Passalis, N., Tefas, A.,
Neural Network Knowledge Transfer using Unsupervised Similarity Matching,
ICPR18(716-721)
IEEE DOI 1812
a Receivers, Knowledge transfer, Training, Knowledge engineering, Neural networks, Computational modeling, Geometry BibRef

Wu, Y.X.[Yu-Xin], He, K.[Kaiming],
Group Normalization,
ECCV18(XIII: 3-19).
Springer DOI 1810
BibRef

Pan, X.G.[Xin-Gang], Luo, P.[Ping], Shi, J.P.[Jian-Ping], Tang, X.[Xiaoou],
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net,
ECCV18(II: 484-500).
Springer DOI 1810
Instance normalization and batch normalization for expanding domain. BibRef

Mallya, A.[Arun], Davis, D.[Dillon], Lazebnik, S.[Svetlana],
Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights,
ECCV18(II: 72-88).
Springer DOI 1810
BibRef

Hou, S.H.[Sai-Hui], Pan, X.Y.[Xin-Yu], Loy, C.C.[Chen Change], Wang, Z.L.[Zi-Lei], Lin, D.H.[Da-Hua],
Lifelong Learning via Progressive Distillation and Retrospection,
ECCV18(III: 452-467).
Springer DOI 1810
BibRef

Cao, Z.J.[Zhang-Jie], Ma, L.J.[Li-Jia], Long, M.S.[Ming-Sheng], Wang, J.M.[Jian-Min],
Partial Adversarial Domain Adaptation,
ECCV18(VIII: 139-155).
Springer DOI 1810
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

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

Koniusz, P.[Piotr], Tas, Y.[Yusuf], Zhang, H.[Hongguang], Harandi, M.[Mehrtash], Porikli, F.M.[Fatih M.], Zhang, R.[Rui],
Museum Exhibit Identification Challenge for the Supervised Domain Adaptation and Beyond,
ECCV18(XVI: 815-833).
Springer DOI 1810
BibRef

Peng, K.C.[Kuan-Chuan], Wu, Z.[Ziyan], Ernst, J.[Jan],
Zero-Shot Deep Domain Adaptation,
ECCV18(XI: 793-810).
Springer DOI 1810
BibRef

Saito, K.[Kuniaki], Yamamoto, S.[Shohei], Ushiku, Y.[Yoshitaka], Harada, T.[Tatsuya],
Open Set Domain Adaptation by Backpropagation,
ECCV18(VI: 156-171).
Springer DOI 1810
BibRef

Huang, H.S.[Hao-Shuo], Huang, Q.X.[Qi-Xing], Krähenbühl, P.[Philipp],
Domain Transfer Through Deep Activation Matching,
ECCV18(XVI: 611-626).
Springer DOI 1810
BibRef

Tao, Q.Y.[Qing-Yi], Yang, H.[Hao], Cai, J.F.[Jian-Fei],
Zero-Annotation Object Detection with Web Knowledge Transfer,
ECCV18(XI: 387-403).
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

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

Pintea, S.L.[Silvia L.], Liu, Y.[Yue], van Gemert, J.C.[Jan C.],
Recurrent Knowledge Distillation,
ICIP18(3393-3397)
IEEE DOI 1809
small network learns from larger network. Computational modeling, Memory management, Training, Color, Convolution, Road transportation, Knowledge distillation, recurrent layers BibRef

Paul, A.[Angshuman], Majumdar, A.[Angshul], Mukherjee, D.P.[Dipti Prasad],
Discriminative Autoencoder,
ICIP18(3049-3053)
IEEE DOI 1809
Minimization, Linear programming, Training, Encoding, Biomedical imaging, Decoding, Autoencoder, discriminative, cross-dataset BibRef

Yeo, D.[Doyeob], Bae, J.R.[Ji-Roon], Kim, N.S.[Nae-Soo], Pyo, C.S.[Cheol-Sig], Yim, J.[Junho], Kim, J.[Junmo],
Sequential Knowledge Transfer in Teacher-Student Framework Using Densely Distilled Flow-Based Information,
ICIP18(674-678)
IEEE DOI 1809
Training, Data mining, Optimization, Image classification, Knowledge transfer, Computational modeling, Reliability, knowledge transfer BibRef

Yan, L., Fan, B., Xiang, S., Pan, C.,
Adversarial Domain Adaptation with a Domain Similarity Discriminator for Semantic Segmentation of Urban Areas,
ICIP18(1583-1587)
IEEE DOI 1809
Urban areas, Semantics, Feature extraction, Image segmentation, Task analysis, Training, Labeling, domain adaptation, domain shift, urban areas BibRef

Inoue, T., Choudhury, S., De Magistris, G., Dasgupta, S.,
Transfer Learning from Synthetic to Real Images Using Variational Autoencoders for Precise Position Detection,
ICIP18(2725-2729)
IEEE DOI 1809
Lighting, Training, Task analysis, Robots, Decoding, Image color analysis, Cameras, deep learning, position detection, computer simulation BibRef

Liu, X., Wang, C., Hu, Y., Zeng, Z., Bai, J., Liao, G.,
Transfer Learning with Convolutional Neural Network for Early Gastric Cancer Classification on Magnifiying Narrow-Band Imaging Images,
ICIP18(1388-1392)
IEEE DOI 1809
Feature extraction, Task analysis, Computer architecture, Cancer, Training, Sensitivity, Convolutional neural networks, convolutional neural networks (CNNs) BibRef

Kim, D.J., Choi, J., Oh, T.H., Yoon, Y., Kweon, I.S.,
Disjoint Multi-task Learning Between Heterogeneous Human-Centric Tasks,
WACV18(1699-1708)
IEEE DOI 1806
learning (artificial intelligence), optimisation, alternating directional optimization method, Visualization 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

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

Wu, G.B.[Guang-Bin], Chen, W.S.[Wei-Shan], Zuo, W.M.[Wang-Meng], Zhang, D.[David],
Unsupervised Domain Adaptation with Robust Deep Logistic Regression,
PSIVT17(199-211).
Springer DOI 1802
BibRef

Bai, T., Yang, J., Chen, J., Guo, X., Huang, X., Yao, Y.,
Double-Task Deep Q-Learning with Multiple Views,
CEFR-LCV17(1050-1058)
IEEE DOI 1802
Aerospace electronics, Cameras, Robot kinematics, Robot vision systems, Training BibRef

Chang, J.H.R.[J.H. Rick], Li, C.L.[Chun-Liang], Póczos, B.[Barnabás], Kumar, B.V.K.V.[B. V. K. Vijaya],
One Network to Solve Them All: Solving Linear Inverse Problems Using Deep Projection Models,
ICCV17(5889-5898)
IEEE DOI 1802
compressed sensing, image restoration, inverse problems, iterative methods, learning (artificial intelligence), Signal resolution BibRef

Li, D., Yang, Y., Song, Y.Z., Hospedales, T.M.,
Deeper, Broader and Artier Domain Generalization,
ICCV17(5543-5551)
IEEE DOI 1802
learning (artificial intelligence), DG alternatives, DG benchmark dataset, DG methods, benchmarks, Painting BibRef

Kim, K.I.[Kwang In], Tompkin, J.[James], Richardt, C.[Christian],
Predictor Combination at Test Time,
ICCV17(3573-3581)
IEEE DOI 1802
differential geometry, image denoising, learning (artificial intelligence), prediction theory, Training BibRef

Haeusser, P., Frerix, T., Mordvintsev, A., Cremers, D.[Daniel],
Associative Domain Adaptation,
ICCV17(2784-2792)
IEEE DOI 1802
convolution, neural net architecture, pattern classification, statistical analysis, association loss, Training BibRef

Masana, M., van de Weijer, J.[Joost], Herranz, L., Bagdanov, A.D., Álvarez, J.M.,
Domain-Adaptive Deep Network Compression,
ICCV17(4299-4307)
IEEE DOI 1802
image coding, image representation, learning (artificial intelligence), matrix decomposition, Training BibRef

Motiian, S., Piccirilli, M., Adjeroh, D.A., Doretto, G.,
Unified Deep Supervised Domain Adaptation and Generalization,
ICCV17(5716-5726)
IEEE DOI 1802
feature extraction, image representation, learning (artificial intelligence), statistical distributions, Visualization BibRef

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

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

Aygün, M., Aytar, Y., Ekenel, H.K.,
Exploiting Convolution Filter Patterns for Transfer Learning,
TASKCV17(2674-2680)
IEEE DOI 1802
Analytical models, Computer architecture, Convolution, Covariance matrices, Detectors, 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

Devi, V.S.[V. Sowmini], Padmanabhan, V.[Vineet], Pujari, A.K.[Arun K.],
A Matrix Factorization & Clustering Based Approach for Transfer Learning,
PReMI17(77-83).
Springer DOI 1711
BibRef

Aljundi, R.[Rahaf], Chakravarty, P.[Punarjay], Tuytelaars, T.[Tinne],
Expert Gate: Lifelong Learning with a Network of Experts,
CVPR17(7120-7129)
IEEE DOI 1711
Data models, Load modeling, Logic gates, Neural networks, Training, Training, data BibRef

Novotny, D.[David], Larlus, D.[Diane], Vedaldi, A.[Andrea],
AnchorNet: A Weakly Supervised Network to Learn Geometry-Sensitive Features for Semantic Matching,
CVPR17(2867-2876)
IEEE DOI 1711
Apply across tasks. Geometry, Proposals, Reliability, Semantics, Visualization. BibRef

Zhang, J., Li, W., Ogunbona, P.,
Joint Geometrical and Statistical Alignment for Visual Domain Adaptation,
CVPR17(5150-5158)
IEEE DOI 1711
Image recognition, Linear programming, Minimization, Pattern recognition, Training, Visualization 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

Yan, H., Ding, Y., Li, P., Wang, Q., Xu, Y., Zuo, W.,
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

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

Ge, W., Yu, Y.,
Borrowing Treasures from the Wealthy: Deep Transfer Learning through Selective Joint Fine-Tuning,
CVPR17(10-19)
IEEE DOI 1711
Kernel, Machine learning, Neural networks, Training data, Visualization BibRef

Yim, J., Joo, D., Bae, J., Kim, J.,
A Gift from Knowledge Distillation: Fast Optimization, Network Minimization and Transfer Learning,
CVPR17(7130-7138)
IEEE DOI 1711
Feature extraction, Knowledge engineering, Knowledge transfer, Optimization, Training BibRef

Chen, S., Zhou, F., Liao, Q.,
Visual domain adaptation using weighted subspace alignment,
VCIP16(1-4)
IEEE DOI 1701
Feature extraction BibRef

Yu, L.J.[Liang-Jiang], Fan, G.L.[Guo-Liang],
Rare Class Oriented Scene Labeling Using CNN Incorporated Label Transfer,
ISVC16(I: 309-320).
Springer DOI 1701
BibRef

Gupta, S.[Saurabh], Hoffman, J.[Judy], Malik, J.[Jitendra],
Cross Modal Distillation for Supervision Transfer,
CVPR16(2827-2836)
IEEE DOI 1612
BibRef

Littwin, E., Wolf, L.,
Complexity of multiverse networks and their multilayer generalization,
ICPR16(372-377)
IEEE DOI 1705
Complexity theory, Feeds, Joining processes, Neural networks, Nonhomogeneous media, Training, Upper, bound BibRef

Littwin, E., Wolf, L.,
The Multiverse Loss for Robust Transfer Learning,
CVPR16(3957-3966)
IEEE DOI 1612
BibRef

Sharmanska, V., Quadrianto, N.,
Learning from the Mistakes of Others: Matching Errors in Cross-Dataset Learning,
CVPR16(3967-3975)
IEEE DOI 1612
BibRef

Chu, B.[Brian], Madhavan, V.[Vashisht], Beijbom, O.[Oscar], Hoffman, J.[Judy], Darrell, T.J.[Trevor J.],
Best Practices for Fine-Tuning Visual Classifiers to New Domains,
TASKCV16(III: 435-442).
Springer DOI 1611
Fine tuned generic to specific domain. 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

Heravi, E.J.[Elnaz Jahani], Aghdam, H.H.[Hamed Habibi], Puig, D.[Domenec],
Training a Mentee Network by Transferring Knowledge from a Mentor Network,
TASKCV16(III: 500-507).
Springer DOI 1611
BibRef

Yan, Y.G.[Yu-Guang], Wu, Q.Y.[Qing-Yao], Tan, M.[Mingkui], Min, H.Q.[Hua-Qing],
Online Heterogeneous Transfer Learning by Weighted Offline and Online Classifiers,
TASKCV16(III: 467-474).
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

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

Sun, B., Peng, X.C.[Xing-Chao], Yu, S.X., Saenko, K.,
Ground2sky label transfer for fine-grained aerial car recognition,
ICIP17(360-364)
IEEE DOI 1803
Automobiles, Google, Image color analysis, Image recognition, Image resolution, Object recognition, Task analysis, Label Transfer BibRef

Peng, X.C.[Xing-Chao], Hoffman, J., Yu, S.X., Saenko, K.[Kate],
Fine-to-coarse knowledge transfer for low-res image classification,
ICIP16(3683-3687)
IEEE DOI 1610
Automobiles BibRef

Pilanci, M., Vural, E.,
Domain adaptation via transferring spectral properties of label functions on graphs,
IVMSP16(1-5)
IEEE DOI 1608
Coherence BibRef

Liu, S.[Siqi], Kovashka, A.[Adriana],
Adapting attributes by selecting features similar across domains,
WACV16(1-8)
IEEE DOI 1606
Adaptation models. Adapt attributes to different domain. BibRef

Irie, G.[Go], Arai, H.[Hiroyuki], Taniguchi, Y.[Yukinobu],
Alternating Co-Quantization for Cross-Modal Hashing,
ICCV15(1886-1894)
IEEE DOI 1602
Binary codes BibRef

Shkodrani, S.[Sindi], Hofmann, M.[Michael], Gavves, E.[Efstratios],
Dynamic Adaptation on Non-stationary Visual Domains,
TASKCV18(II:158-171).
Springer DOI 1905
BibRef

Gavves, E., Mensink, T., Tommasi, T., Snoek, C.G.M., Tuytelaars, T.,
Active Transfer Learning with Zero-Shot Priors: Reusing Past Datasets for Future Tasks,
ICCV15(2731-2739)
IEEE DOI 1602
Computer vision BibRef

Paul, A., Rottensteiner, F., Heipke, C.,
Transfer Learning Based on Logistic Regression,
CMRT15(145-152).
DOI Link 1602
BibRef

Liu, P.C.[Peng-Cheng], Wang, C.[Chong], Yang, P.[Peipei], Huang, K.Q.[Kai-Qi], Tan, T.N.[Tie-Niu],
Cross-Domain Object Recognition Using Object Alignment,
BMVC15(xx-yy).
DOI Link 1601
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
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ICIP15(3461-3465)
IEEE DOI 1512
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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

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

Pentina, A.[Anastasia], Sharmanska, V.[Viktoriia], Lampert, C.H.[Christoph H.],
Curriculum learning of multiple tasks,
CVPR15(5492-5500)
IEEE DOI 1510
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Rochan, M.[Mrigank], Wang, Y.[Yang],
Weakly supervised localization of novel objects using appearance transfer,
CVPR15(4315-4324)
IEEE DOI 1510
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Dai, D.X.[Deng-Xin], Kroeger, T.[Till], Timofte, R.[Radu], Van Gool, L.J.[Luc J.],
Metric imitation by manifold transfer for efficient vision applications,
CVPR15(3527-3536)
IEEE DOI 1510
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Niu, L.[Li], Li, W.[Wen], Xu, D.[Dong],
Visual recognition by learning from web data: A weakly supervised domain generalization approach,
CVPR15(2774-2783)
IEEE DOI 1510
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Isola, P.[Phillip], Lim, J.J.[Joseph J.], Adelson, E.H.[Edward H.],
Discovering states and transformations in image collections,
CVPR15(1383-1391)
IEEE DOI 1510
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Royer, A.[Amelie], Lampert, C.H.[Christoph H.],
Classifier adaptation at prediction time,
CVPR15(1401-1409)
IEEE DOI 1510
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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
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Ciliberto, C.[Carlo], Rosasco, L.[Lorenzo], Villa, S.[Silvia],
Learning multiple visual tasks while discovering their structure,
CVPR15(131-139)
IEEE DOI 1510
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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
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Manteghi, S.[Sajad], Parvin, H.[Hamid], Heidarzadegan, A.[Ali], Nemati, Y.[Yasser],
Multitask Reinforcement Learning in Nondeterministic Environments: Maze Problem Case,
MCPR15(64-73).
Springer DOI 1506
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Ranjan, V.[Viresh], Harit, G.[Gaurav], Jawahar, C.V.,
Learning Partially Shared Dictionaries for Domain Adaptation,
FSLCV14(III: 247-261).
Springer DOI 1504
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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
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Lu, Y.[Ying], Chen, L.M.[Li-Ming], Saidi, A.[Alexandre], Zhang, Z.X.[Zhao-Xiang], Wang, Y.H.[Yun-Hong],
Learning visual categories through a sparse representation classifier based cross-category knowledge transfer,
ICIP14(165-169)
IEEE DOI 1502
Computer vision BibRef

Kim, J.[Jongdae], Collomosse, J.P.[John P.],
Incremental transfer learning for object recognition in streaming video,
ICIP14(2729-2733)
IEEE DOI 1502
Kernel BibRef

Wang, Z.H.[Zi-Heng], Gao, T.[Tian], Ji, Q.A.[Qi-Ang],
Learning with Hidden Information Using a Max-Margin Latent Variable Model,
ICPR14(1389-1394)
IEEE DOI 1412
Data models BibRef

Zhou, Y.[Yun], Ioannou, I.[Ioanna], Wijewickrema, S.[Sudanthi], Bailey, J.[James], Piromchai, P.[Patorn], Kennedy, G.[Gregor], OLeary, S.[Stephen],
Transfer Learning of a Temporal Bone Performance Model via Anatomical Feature Registration,
ICPR14(1916-1921)
IEEE DOI 1412
Adaptation models BibRef

Fang, Z.[Zheng], Zhang, Z.F.[Zhong-Fei],
Cross Domain Shared Subspace Learning for Unsupervised Transfer Classification,
ICPR14(3927-3932)
IEEE DOI 1412
Accuracy BibRef

Samanta, S.[Suranjana], Selvan, T.[Tirumarai], Das, S.[Sukhendu],
Modeling Sequential Domain Shift through Estimation of Optimal Sub-spaces for Categorization,
BMVC14(xx-yy).
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Farajidavar, N.[Nazli], de Campos, T.[Teofilo], Kittler, J.V.[Josef V.],
Transductive Transfer Machine,
ACCV14(III: 623-639).
Springer DOI 1504
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Adaptive Transductive Transfer Machine,
BMVC14(xx-yy).
HTML Version. 1410
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Amaral, T.[Telmo], Silva, L.M.[Luís M.], Alexandre, L.A.[Luís A.], Kandaswamy, C.[Chetak], de Sá, J.M.[Joaquim Marques], Santos, J.M.[Jorge M.],
Transfer Learning Using Rotated Image Data to Improve Deep Neural Network Performance,
ICIAR14(I: 290-300).
Springer DOI 1410
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Branson, S.[Steve], Hjorleifsson, K.E.[Kristjan Eldjarn], Perona, P.[Pietro],
Active Annotation Translation,
CVPR14(3702-3709)
IEEE DOI 1409
active learning. Quickly annotate when previous annotations exist. BibRef

Lapin, M.[Maksim], Schiele, B.[Bernt], Hein, M.[Matthias],
Scalable Multitask Representation Learning for Scene Classification,
CVPR14(1434-1441)
IEEE DOI 1409
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Zhang, B.[Bang], Wang, Y.[Yi], Wang, Y.[Yang], Chen, F.[Fang],
Stable Learning in Coding Space for Multi-class Decoding and Its Extension for Multi-class Hypothesis Transfer Learning,
CVPR14(1075-1081)
IEEE DOI 1409
Multi-class classification; output coding framework; transfer learning 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

Tsuchiya, M., Yamauchi, Y., Fujiyoshi, H., Yamashita, T.,
Hybrid Transfer Learning for Efficient Learning in Object Detection,
ACPR13(69-73)
IEEE DOI 1408
feature extraction BibRef

Rana, A.[Aakanksha], Zepeda, J.[Joaquin], Perez, P.[Patrick],
Feature Learning for the Image Retrieval Task,
FSLCV14(III: 152-165).
Springer DOI 1504
BibRef

Kulkarni, P.[Praveen], Zepeda, J.[Joaquin], Jurie, F.[Frederic], Pérez, P.[Patrick], Chevallier, L.[Louis],
Learning the Structure of Deep Architectures Using L1 Regularization,
BMVC15(xx-yy).
DOI Link 1601
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Kulkarni, P.[Praveen], Sharma, G.[Gaurav], Zepeda, J.[Joaquin], Chevallier, L.[Louis],
Transfer learning via attributes for improved on-the-fly classification,
WACV14(220-226)
IEEE DOI 1406
Animals 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

Long, M.S.[Ming-Sheng], Ding, G.G.[Gui-Guang], Wang, J.M.[Jian-Min], Sun, J.G.[Jia-Guang], Guo, Y.C.[Yu-Chen], Yu, P.S.[Philip S.],
Transfer Sparse Coding for Robust Image Representation,
CVPR13(407-414)
IEEE DOI 1309
image representation; sparse coding; transfer learning BibRef

Kobetski, M.[Miroslav], Sullivan, J.[Josephine],
Discriminative tree-based feature mapping,
BMVC13(xx-yy).
DOI Link 1402
BibRef

Kobetski, M.[Miroslav], Sullivan, J.[Josephine],
Apprenticeship Learning: Transfer of Knowledge via Dataset Augmentation,
SCIA13(432-443).
Springer DOI 1311
BibRef

Garcia, E.O.[Esteban O.], de Cote, E.M.[Enrique Munoz], Morales, E.F.[Eduardo F.],
Qualitative Transfer for Reinforcement Learning with Continuous State and Action Spaces,
CIARP13(I:198-205).
Springer DOI 1311
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Chen, S.K.[Shao-Kang], Sanderson, C.[Conrad], Harandi, M.T.[Mehrtash T.], Lovell, B.C.[Brian C.],
Improved Image Set Classification via Joint Sparse Approximated Nearest Subspaces,
CVPR13(452-459)
IEEE DOI 1309
Adaptive Clustering BibRef

Kuzborskij, I.[Ilja], Orabona, F.[Francesco], Caputo, B.[Barbara],
Transfer Learning Through Greedy Subset Selection,
CIAP15(I:3-14).
Springer DOI 1511
BibRef
Earlier:
From N to N+1: Multiclass Transfer Incremental Learning,
CVPR13(3358-3365)
IEEE DOI 1309
LSSVM BibRef

Sharmanska, V.[Viktoriia], Quadrianto, N.[Novi], Lampert, C.H.[Christoph H.],
Learning to Rank Using Privileged Information,
ICCV13(825-832)
IEEE DOI 1403
Learning to rank with more data in training than in test. BibRef

Tommasi, T.[Tatiana], Quadrianto, N.[Novi], Caputo, B.[Barbara], Lampert, C.H.[Christoph H.],
Beyond Dataset Bias: Multi-task Unaligned Shared Knowledge Transfer,
ACCV12(I:1-15).
Springer DOI 1304
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).
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shifts in the distribution between training and testing data 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

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

Liu, J.C.[Jing-Chen], McCloskey, S.[Scott], Liu, Y.X.[Yan-Xi],
Training data recycling for multi-level learning,
ICPR12(2314-2318).
WWW Link. 1302
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Shi, Z.Y.[Zhi-Yuan], Siva, P.[Parthipan], Xiang, T.[Tony],
Transfer Learning by Ranking for Weakly Supervised Object Annotation,
BMVC12(78).
DOI Link 1301
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Costa Pereira, J.[Jose], Vasconcelos, N.M.[Nuno M.],
On the regularization of image semantics by modal expansion,
CVPR12(3093-3099).
IEEE DOI 1208
BibRef

Guillaumin, M.[Matthieu], Ferrari, V.[Vittorio],
Large-scale knowledge transfer for object localization in ImageNet,
CVPR12(3202-3209).
IEEE DOI 1208
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

Ye, G.N.[Guang-Nan], Liu, D.[Dong], Jhuo, I.H.[I-Hong], Chang, S.F.[Shih-Fu],
Robust late fusion with rank minimization,
CVPR12(3021-3028).
IEEE DOI 1208
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Aytar, Y.[Yusuf], Zisserman, A.[Andrew],
Tabula rasa: Model transfer for object category detection,
ICCV11(2252-2259).
IEEE DOI 1201
Transfer learning for categories. BibRef

Wang, H.[Hua], Nie, F.P.[Fei-Ping], Huang, H.[Heng], Ding, C.[Chris],
Dyadic transfer learning for cross-domain image classification,
ICCV11(551-556).
IEEE DOI 1201
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

Yu, X.D.[Xiao-Dong], Aloimonos, Y.[Yiannis],
Attribute-Based Transfer Learning for Object Categorization with Zero/One Training Example,
ECCV10(V: 127-140).
Springer DOI 1009
BibRef

Yao, Y.[Yi], Doretto, G.[Gianfranco],
Boosting for transfer learning with multiple sources,
CVPR10(1855-1862).
IEEE DOI 1006
Transfer info between domains. BibRef

Lampert, C.H.[Christoph H.], Krömer, O.[Oliver],
Weakly-Paired Maximum Covariance Analysis for Multimodal Dimensionality Reduction and Transfer Learning,
ECCV10(II: 566-579).
Springer DOI 1009
BibRef

Ahmed, A.[Amr], Yu, K.[Kai], Xu, W.[Wei], Gong, Y.H.[Yi-Hong], Xing, E.[Eric],
Training Hierarchical Feed-Forward Visual Recognition Models Using Transfer Learning from Pseudo-Tasks,
ECCV08(III: 69-82).
Springer DOI 0810
BibRef

Quattoni, A.[Ariadna], Collins, M.[Michael], Darrell, T.J.[Trevor J.],
Transfer learning for image classification with sparse prototype representations,
CVPR08(1-8).
IEEE DOI 0806
BibRef
And: CSAIL-2008-012, March 2008.
WWW Link. BibRef
Earlier:
Learning Visual Representations using Images with Captions,
CVPR07(1-8).
IEEE DOI 0706
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Zero-Shot Learning .