14.1.6.2 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.Y.[Hao-Yuan], 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

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.M.[Hong-Min], 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.C.[Liang-Chen], 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

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

Yang, H.W.[Hong-Wei], He, H.[Hui], Li, T.[Tao], Bai, Y.[Yawen], Zhang, W.Z.[Wei-Zhe],
Multi-metric domain adaptation for unsupervised transfer learning,
IET-IPR(14), No. 12, October 2020, pp. 2780-2790.
DOI Link 2010
BibRef

Zhao, Z.C.[Zhi-Cheng], Luo, Z.[Ze], Li, J.[Jian], Chen, C.[Can], Piao, Y.C.[Ying-Chao],
When Self-Supervised Learning Meets Scene Classification: Remote Sensing Scene Classification Based on a Multitask Learning Framework,
RS(12), No. 20, 2020, pp. xx-yy.
DOI Link 2010
BibRef

Sun, X.W.[Xin-Wei], Xu, Y.L.[Yi-Lun], Cao, P.[Peng], Kong, Y.Q.[Yu-Qing], Hu, L.J.[Ling-Jing], Zhang, S.H.[Shang-Hang], Wang, Y.Z.[Yi-Zhou],
TCGM: An Information-theoretic Framework for Semi-supervised Multi-modality Learning,
ECCV20(III:171-188).
Springer DOI 2012
BibRef

Alvar, S.R., Bajic, I.V.,
Pareto-Optimal Bit Allocation for Collaborative Intelligence,
IP(30), 2021, pp. 3348-3361.
IEEE DOI 2103
BibRef
Earlier:
Multi-Task Learning with Compressible Features for Collaborative Intelligence,
ICIP19(1705-1709)
IEEE DOI 1910
Tensors, Task analysis, Image coding, Bit rate, Nonlinear distortion, Distortion measurement, Optimization, Bit allocation, multi-task learning. collaborative intelligence, deep feature compression BibRef

Zhang, R.[Rui], Zhang, H.Y.[Hong-Yuan], Li, X.L.[Xue-Long],
Robust Multi-Task Learning With Flexible Manifold Constraint,
PAMI(43), No. 6, June 2021, pp. 2150-2157.
IEEE DOI 2106
Task analysis, Manifolds, Adaptation models, Robustness, Training, Computational modeling, Predictive models, Multi-task learning, regression BibRef

Li, J.[Jie], Huang, L.[Lei], Wei, Z.Q.[Zhi-Qiang], Zhang, W.F.[Wen-Feng], Qin, Q.B.[Qi-Bing],
Multi-task learning with deformable convolution,
JVCIR(77), 2021, pp. 103109.
Elsevier DOI 2106
Multi-task learning, Deformable convolution, Recognition BibRef

Tomar, D.[Devavrat], Lortkipanidze, M.[Manana], Vray, G.[Guillaume], Bozorgtabar, B.[Behzad], Thiran, J.P.[Jean-Philippe],
Self-Attentive Spatial Adaptive Normalization for Cross-Modality Domain Adaptation,
MedImg(40), No. 10, October 2021, pp. 2926-2938.
IEEE DOI 2110
Image segmentation, Biomedical imaging, Computed tomography, Magnetic resonance imaging, Semantics, Anatomical structure, self-attention BibRef

Yang, Z.Y.[Zhi-Yong], Xu, Q.Q.[Qian-Qian], Cao, X.C.[Xiao-Chun], Huang, Q.M.[Qing-Ming],
Task-Feature Collaborative Learning with Application to Personalized Attribute Prediction,
PAMI(43), No. 11, November 2021, pp. 4094-4110.
IEEE DOI 2110
Task analysis, Convergence, Predictive models, Diseases, Collaborative work, Optimization, Training, global convergence BibRef

Caldarola, D.[Debora], Mancini, M.[Massimiliano], Galasso, F.[Fabio], Ciccone, M.[Marco], Rodolà, E.[Emanuele], Caputo, B.[Barbara],
Cluster-driven Graph Federated Learning over Multiple Domains,
LLID21(2743-2752)
IEEE DOI 2109
Training, Knowledge engineering, Computational modeling, Benchmark testing, Collaborative work, Data models, Iterative algorithms BibRef

Khattar, A.[Apoorv], Hegde, S.[Srinidhi], Hebbalaguppe, R.[Ramya],
Cross-Domain Multi-task Learning for Object Detection and Saliency Estimation,
OmniCV21(3634-3643)
IEEE DOI 2109
Training, Neural networks, Estimation, Object detection, Computer architecture, Pattern recognition BibRef

Wang, Q.[Qifei], Ke, J.J.[Jun-Jie], Greaves, J.[Joshua], Chu, G.[Grace], Bender, G.[Gabriel], Sbaiz, L.[Luciano], Go, A.[Alec], Howard, A.[Andrew], Yang, M.H.[Ming-Hsuan], Gilbert, J.[Jeff], Milanfar, P.[Peyman], Yang, F.[Feng],
Multi-path Neural Networks for On-device Multi-domain Visual Classification,
WACV21(3018-3027)
IEEE DOI 2106
Training, Visualization, Adaptation models, Computational modeling, Scalability, Interference, Reinforcement learning BibRef

Li, Z.Z.[Zhi-Zhong], Luo, L.J.[Lin-Jie], Tulyakov, S.[Sergey], Dai, Q.[Qieyun], Hoiem, D.[Derek],
Task-Assisted Domain Adaptation with Anchor Tasks,
WACV21(2988-2997)
IEEE DOI 2106
Training, Image segmentation, Shape, Annotations, Semantics BibRef

Frecon, J.[Jordan], Salzo, S.[Saverio], Pontil, M.[Massimiliano],
Unveiling Groups of Related Tasks in Multi-Task Learning,
ICPR21(7134-7141)
IEEE DOI 2105
Benchmark testing, Approximation algorithms, Pattern recognition, Computational efficiency, Task analysis, Optimization, Standards BibRef

Yang, S.M.[Shih-Min], Yeh, M.C.[Mei-Chen],
Unsupervised Multi-Task Domain Adaptation,
ICPR21(1679-1685)
IEEE DOI 2105
Adaptation models, Image recognition, Target recognition, Annotations, Training data, Image representation, Pattern recognition BibRef

He, X.X.[Xin-Xing], Yuan, Y.[Yuan], Jiang, Z.[Zhiyu],
Open Set Domain Recognition via Attention-Based GCN and Semantic Matching Optimization,
ICPR21(4626-4633)
IEEE DOI 2105
Training, Limiting, Image recognition, Target recognition, Semantics, Training data, Network architecture, open set domain recognition, semantic matching BibRef

Spadotto, T.[Teo], Toldo, M.[Marco], Michieli, U.[Umberto], Zanuttigh, P.[Pietro],
Unsupervised Domain Adaptation with Multiple Domain Discriminators and Adaptive Self-Training,
ICPR21(2845-2852)
IEEE DOI 2105
Adaptation models, Roads, Semantics, Neural networks, Reliability engineering, Robustness, Data models BibRef

Senhaji, A.[Ali], Raitoharju, J.[Jenni], Gabbouj, M.[Moncef], Iosifidis, A.[Alexandros],
Not all domains are equally complex: Adaptive Multi-Domain Learning,
ICPR21(8663-8670)
IEEE DOI 2105
Training, Deep learning, Adaptation models, Adaptive systems, Neural networks, Pattern recognition, Complexity theory BibRef

Takeda, M.[Mana], Benitez, G.[Gibran], Yanai, K.[Keiji],
Training of Multiple and Mixed Tasks with a Single Network Using Feature Modulation,
DLPR20(719-735).
Springer DOI 2103
BibRef

Zhu, R., Yan, L.,
Neighbour-based Domain Adaptation for Investigation of Transferable Ability of Previously Labeled Data for Land-cover Classification Of Aerial Images,
ISPRS20(B2:1329-1335).
DOI Link 2012
BibRef

Jain, H.[Himalaya], Gidaris, S.[Spyros], Komodakis, N.[Nikos], Pérez, P.[Patrick], Cord, M.[Matthieu],
Quest: Quantized Embedding Space for Transferring Knowledge,
ECCV20(XXI:173-189).
Springer DOI 2011
BibRef

Kanakis, M.[Menelaos], Bruggemann, D.[David], Saha, S.[Suman], Georgoulis, S.[Stamatios], Obukhov, A.[Anton], Van Gool, L.J.[Luc J.],
Reparameterizing Convolutions for Incremental Multi-Task Learning Without Task Interference,
ECCV20(XX:689-707).
Springer DOI 2011
BibRef

Huang, Z.Y.[Ze-Yi], Wang, H.H.[Hao-Han], Xing, E.P.[Eric P.], Huang, D.[Dong],
Self-challenging Improves Cross-domain Generalization,
ECCV20(II:124-140).
Springer DOI 2011
BibRef

Katzir, O.[Oren], Lischinski, D.[Dani], Cohen-Or, D.[Daniel],
Cross-domain Cascaded Deep Translation,
ECCV20(II:673-689).
Springer DOI 2011
BibRef

Vandenhende, S.[Simon], Georgoulis, S.[Stamatios], Van Gool, L.J.[Luc J.],
Mti-net: Multi-scale Task Interaction Networks for Multi-task Learning,
ECCV20(IV:527-543).
Springer DOI 2011
BibRef

Tschannen, M.[Michael], Djolonga, J.[Josip], Ritter, M.[Marvin], Mahendran, A.[Aravindh], Houlsby, N.[Neil], Gelly, S.[Sylvain], Lucic, M.[Mario],
Self-Supervised Learning of Video-Induced Visual Invariances,
CVPR20(13803-13812)
IEEE DOI 2008
Videos, Task analysis, Visualization, Adaptation models, Data models, Benchmark testing, Image representation BibRef

Zhou, L., Cui, Z., Xu, C., Zhang, Z., Wang, C., Zhang, T., Yang, J.,
Pattern-Structure Diffusion for Multi-Task Learning,
CVPR20(4513-4522)
IEEE DOI 2008
Task analysis, Estimation, Image segmentation, Correlation, Semantics, Decoding, Sparse matrices BibRef

Lu, J., Goswami, V., Rohrbach, M., Parikh, D., Lee, S.,
12-in-1: Multi-Task Vision and Language Representation Learning,
CVPR20(10434-10443)
IEEE DOI 2008
Task analysis, Training, Visualization, Grounding, Image retrieval, Predictive models, Knowledge discovery BibRef

Wang, W., Tran, D., Feiszli, M.,
What Makes Training Multi-Modal Classification Networks Hard?,
CVPR20(12692-12702)
IEEE DOI 2008
Training, Task analysis, Kinetic theory, Optimization, Visualization, Computer vision, Benchmark testing BibRef

Jha, A., Kumar, A., Banerjee, B., Chaudhuri, S.,
AdaMT-Net: An Adaptive Weight Learning Based Multi-Task Learning Model For Scene Understanding,
EDLCV20(3027-3035)
IEEE DOI 2008
Task analysis, Decoding, Training, Adaptation models, Estimation, Semantics, Image segmentation BibRef

Choi, S., Hong, S., Lee, K., Lim, S.,
Task Agnostic Robust Learning on Corrupt Outputs by Correlation-Guided Mixture Density Networks,
CVPR20(3871-3880)
IEEE DOI 2008
Training, Robustness, Correlation, Task analysis, Noise measurement, Training data, Neural networks BibRef

Xia, Y., Liu, F., Yang, D., Cai, J., Yu, L., Zhu, Z., Xu, D., Yuille, A.L., Roth, H.,
3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training,
WACV20(3635-3644)
IEEE DOI 2006
Training, Biomedical imaging, Task analysis, Uncertainty, Solid modeling 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.F.[Rodrigo F.], 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.[Felix], Tanno, R.[Ryutaro], Ourselin, S.[Sebastien], Alexander, D.[Daniel], Cardoso, J.[Jorge],
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

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

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
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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 .