14.1.4.4.1 Multi-Task Learning, Multiple Tasks, Transfer Learning, Domain Adaption

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

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

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

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

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

Wang, D.H.[Dong-Hui], Kong, S.[Shu],
A classification-oriented dictionary learning model: Explicitly learning the particularity and commonality across categories,
PR(47), No. 2, 2014, pp. 885-898.
Elsevier DOI 1311
BibRef
Earlier: A2, A1:
Transfer heterogeneous unlabeled data for unsupervised clustering,
ICPR12(1193-1196).
WWW Link. 1302
BibRef
Earlier: A2, A1:
A multi-task learning strategy for unsupervised clustering via explicitly separating the commonality,
ICPR12(771-774).
WWW Link. 1302
Dictionary learning BibRef

Abdulnabi, A.H., Wang, G., Lu, J., Jia, K.,
Multi-Task CNN Model for Attribute Prediction,
MultMed(17), No. 11, November 2015, pp. 1949-1959.
IEEE DOI 1511
Clothing BibRef

Li, X., Zhao, L., Wei, L., Yang, M.H., Wu, F., Zhuang, Y., Ling, H., Wang, J.,
DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection,
IP(25), No. 8, August 2016, pp. 3919-3930.
IEEE DOI 1608
Computational modeling BibRef

Fan, J.P.[Jian-Ping], Zhao, T.Y.[Tian-Yi], Kuang, Z.Z.[Zhen-Zhong], Zheng, Y.[Yu], Zhang, J.[Ji], Yu, J.[Jun], Peng, J.Y.[Jin-Ye],
HD-MTL: Hierarchical Deep Multi-Task Learning for Large-Scale Visual Recognition,
IP(26), No. 4, April 2017, pp. 1923-1938.
IEEE DOI 1704
Atomic layer deposition. Large-scale. BibRef

Liu, T., Tao, D., Song, M., Maybank, S.J.,
Algorithm-Dependent Generalization Bounds for Multi-Task Learning,
PAMI(39), No. 2, February 2017, pp. 227-241.
IEEE DOI 1702
Algorithm design and analysis BibRef

Zhang, Y.X.[Yu-Xiang], Wu, K.[Ke], Du, B.[Bo], Zhang, L.P.[Liang-Pei], Hu, X.Y.[Xiang-Yun],
Hyperspectral Target Detection via Adaptive Joint Sparse Representation and Multi-Task Learning with Locality Information,
RS(9), No. 5, 2017, pp. xx-yy.
DOI Link 1706
See also Joint Sparse Representation and Multitask Learning for Hyperspectral Target Detection. BibRef

Dong, Y.N.[Yan-Ni], Zhang, L.P.[Liang-Pei], Zhang, L.F.[Le-Fei], Du, B.[Bo],
Maximum Margin Metric Learning Based Target Detection for Hyperspectral Images,
PandRS(108), No. 1, 2015, pp. 138-150.
Elsevier DOI 1511
Target detection BibRef

Dong, Y.N.[Yan-Ni], Du, B.[Bo], Zhang, L.P.[Liang-Pei], Hu, X.Y.[Xiang-Yun],
Hyperspectral Target Detection via Adaptive Information-Theoretic Metric Learning with Local Constraints,
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link 1810
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

Kuang, Z.Z.[Zhen-Zhong], Yu, J.[Jun], Li, Z.M.[Zong-Min], Zhang, B.P.[Bao-Peng], Fan, J.P.[Jian-Ping],
Integrating multi-level deep learning and concept ontology for large-scale visual recognition,
PR(78), 2018, pp. 198 - 214.
Elsevier DOI 1804
Large-scale visual recognition, Multi-level deep learning, Multiple deep networks, Concept ontology, Multi-task learning, Tree classifier 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

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

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

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

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

Li, X.H.[Xu-Hong], Grandvalet, Y.[Yves], Davoine, F.[Franck], Cheng, J.C.[Jing-Chun], Cui, Y.[Yin], Zhang, H.[Hang], Belongie, S.[Serge], Tsai, Y.H.[Yi-Hsuan], Yang, M.H.[Ming-Hsuan],
Transfer learning in computer vision tasks: Remember where you come from,
IVC(93), 2020, pp. 103853.
Elsevier DOI 2001
Transfer learning, Parameter regularization, Computer vision BibRef

Chen, Z.[Zhe], Wang, R.[Ruili], Yu, M.[Ming], Gao, H.[Hongmin], Li, Q.[Qi], Wang, H.[Huibin],
Spatial-temporal multi-task learning for salient region detection,
PRL(132), 2020, pp. 76-83.
Elsevier DOI 2005
BibRef

Liu, D.[Deyin], Liu, L.[Liangchen], Tie, Y.[Yun], Qi, L.[Lin],
Multi-task image set classification via joint representation with class-level sparsity and intra-task low-rankness,
PRL(132), 2020, pp. 99-105.
Elsevier DOI 2005
Multi-task recognition, Image set classification, Class-level sparsity, Low-rankness BibRef

Lu, G.Q.[Guang-Quan], Zhao, X.S.[Xi-Shun], Yin, J.[Jian], Yang, W.W.[Wei-Wei], Li, B.[Bo],
Multi-task learning using variational auto-encoder for sentiment classification,
PRL(132), 2020, pp. 115-122.
Elsevier DOI 2005
Sentiment classification, Opinion mining, Deep learning, Multi-task learning, Variational auto-encoder, LSTM, Big data BibRef

He, H.X.[Han-Xian], Khoshelham, K.[Kourosh], Fraser, C.[Clive],
A multiclass TrAdaBoost transfer learning algorithm for the classification of mobile lidar data,
PandRS(166), 2020, pp. 118-127.
Elsevier DOI 2007
To apply deep learnign to LiDAR data. Not enough training data. VoxNet, TrAdaBoost, Multiclass classification, Point Cloud, 3DCNN, Deep learning, Transfer learning BibRef

Adiyeke, E.[Esra], Baydogan, M.G.[Mustafa Gökçe],
The benefits of target relations: A comparison of multitask extensions and classifier chains,
PR(107), 2020, pp. 107507.
Elsevier DOI 2008
Multitask learning, Multi-objective trees, Stacking, Classifier chains, Ensemble learning BibRef

Yang, P.[Pei], Tan, Q.[Qi], He, J.R.[Jing-Rui],
Complex heterogeneity learning: A theoretical and empirical study,
PR(107), 2020, pp. 107519.
Elsevier DOI 2008
Heterogeneous learning, Multi-task learning, Multi-view learning, Multi-instance learning BibRef

Achille, A.[Alessandro], Lam, M.[Michael], Tewari, R.[Rahul], Ravichandran, A.[Avinash], Maji, S.[Subhransu], Fowlkes, C.[Charless], Soatto, S.[Stefano], Perona, P.[Pietro],
Task2Vec: Task Embedding for Meta-Learning,
ICCV19(6429-6438)
IEEE DOI 2004
Vectorial representations of visual classification tasks which can be used to reason about the nature of those tasks. feature extraction, image classification, learning (artificial intelligence), vectorial representations, BibRef

Strezoski, G.[Gjorgji], van Noord, N.[Nanne], Worring, M.[Marcel],
Many Task Learning With Task Routing,
ICCV19(1375-1384)
IEEE DOI 2004
learning (artificial intelligence), pattern classification, conditional feature-wise transformation, classification tasks, Adaptation models BibRef

Berriel, R., Lathuillere, S., Nabi, M., Klein, T., Oliveira-Santos, T., Sebe, N., Ricci, E.,
Budget-Aware Adapters for Multi-Domain Learning,
ICCV19(382-391)
IEEE DOI 2004
computational complexity, learning (artificial intelligence), network theory (graphs). BibRef

Al-Rawi, M., Valveny, E.,
Compact and Efficient Multitask Learning in Vision, Language and Speech,
CEFRL19(2933-2942)
IEEE DOI 2004
computer vision, image classification, learning (artificial intelligence), speech recognition, image classification BibRef

Song, B.C., Kim, D.H., Lee, S.h.,
Metric-Based Regularization and Temporal Ensemble for Multi-Task Learning using Heterogeneous Unsupervised Tasks,
CEFRL19(2903-2912)
IEEE DOI 2004
learning (artificial intelligence), heterogeneous unsupervised tasks, target task, temporal task ensemble BibRef

Ni, F., Yao, Y.,
Multi-Task Learning via Scale Aware Feature Pyramid Networks and Effective Joint Head,
AutoNUE19(4265-4272)
IEEE DOI 2004
convolutional neural nets, feature extraction, image segmentation, learning (artificial intelligence), instance segmentation BibRef

Bragman, F., Tanno, R., Ourselin, S., Alexander, D., Cardoso, J.,
Stochastic Filter Groups for Multi-Task CNNs: Learning Specialist and Generalist Convolution Kernels,
ICCV19(1385-1394)
IEEE DOI 2004
convolutional neural nets, learning (artificial intelligence), probability, stochastic processes, stochastic filter groups, Cats BibRef

Kundu, J.N., Lakkakula, N., Radhakrishnan, V.B.,
UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation,
ICCV19(1436-1445)
IEEE DOI 2004
generalisation (artificial intelligence), image classification, object detection, unsupervised learning, task-transferability, Adaptation models BibRef

Ahn, C., Kim, E., Oh, S.,
Deep Elastic Networks With Model Selection for Multi-Task Learning,
ICCV19(6528-6537)
IEEE DOI 2004
computer vision, feature selection, image classification, learning (artificial intelligence), neural net architecture, Computer architecture BibRef

Hassani, K., Haley, M.,
Unsupervised Multi-Task Feature Learning on Point Clouds,
ICCV19(8159-8170)
IEEE DOI 2004
feature extraction, graph theory, image classification, image reconstruction, image segmentation, pattern clustering, Convolution BibRef

Qin, C., Wang, L., Zhang, Y., Fu, Y.,
Generatively Inferential Co-Training for Unsupervised Domain Adaptation,
RLQ19(1055-1064)
IEEE DOI 2004
computer vision, image classification, neural nets, unsupervised learning, Inferential BibRef

Maninis, K.K.[Kevis-Kokitsi], Radosavovic, I.[Ilija], Kokkinos, I.[Iasonas],
Attentive Single-Tasking of Multiple Tasks,
CVPR19(1851-1860).
IEEE DOI 2002
Network is trained on multiple tasks, but performs one task at a time. BibRef

Liu, S.[Shikun], Johns, E.[Edward], Davison, A.J.[Andrew J.],
End-To-End Multi-Task Learning With Attention,
CVPR19(1871-1880).
IEEE DOI 2002
BibRef

Kawakami, R.[Rei], Yoshihashi, R.[Ryota], Fukuda, S.[Seiichiro], You, S.[Shaodi], Iida, M.[Makoto], Naemura, T.[Takeshi],
Cross-Connected Networks for Multi-Task Learning of Detection and Segmentation,
ICIP19(3636-3640)
IEEE DOI 1910
Multi-task Learning, Pedestrian Detection, Bird Detection, Semantic Segmentation BibRef

Alvar, S.R., Bajic, I.V.,
Multi-Task Learning with Compressible Features for Collaborative Intelligence,
ICIP19(1705-1709)
IEEE DOI 1910
Multi-task learning, collaborative intelligence, deep feature compression BibRef

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

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

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

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

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

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

Kuga, R.[Ryohei], Kanezaki, A.[Asako], Samejima, M.[Masaki], Sugano, Y.[Yusuke], Matsushita, Y.[Yasuyuki],
Multi-task Learning Using Multi-modal Encoder-Decoder Networks with Shared Skip Connections,
MSF17(403-411)
IEEE DOI 1802
Decoding, Feature extraction, Image segmentation, Neural networks, Semantics, Training BibRef

Ciliberto, C.[Carlo], Rosasco, L.[Lorenzo], Villa, S.[Silvia],
Learning multiple visual tasks while discovering their structure,
CVPR15(131-139)
IEEE DOI 1510
BibRef

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
BibRef

Pentina, A.[Anastasia], Sharmanska, V.[Viktoriia], Lampert, C.H.[Christoph H.],
Curriculum learning of multiple tasks,
CVPR15(5492-5500)
IEEE DOI 1510
BibRef

Lapin, M.[Maksim], Schiele, B.[Bernt], Hein, M.[Matthias],
Scalable Multitask Representation Learning for Scene Classification,
CVPR14(1434-1441)
IEEE DOI 1409
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

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Adversarial Networks for Transfer Learning, Domain Adaption .