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Convex Discriminative Multitask Clustering,
PAMI(37), No. 1, January 2015, pp. 28-40.
IEEE DOI
1412
Bismuth
BibRef
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Elsevier DOI
1008
Boosting; Multi-task learning; Inductive transfer learning; Multiple
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BibRef
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PRL(33), No. 5, 1 April 2012, pp. 568-579.
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1202
Opinion mining; Sentimental classification; Boosting; Transfer
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1410
Multi-task clustering
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PAMI(35), No. 5, May 2013, pp. 1025-1038.
IEEE DOI
1304
BibRef
Li, W.[Wen],
Duan, L.X.[Li-Xin],
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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],
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Co-Labeling for Multi-View Weakly Labeled Learning,
PAMI(38), No. 6, June 2016, pp. 1113-1125.
IEEE DOI
1605
Kernel.
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Wang, D.H.[Dong-Hui],
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A classification-oriented dictionary learning model:
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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).
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1302
BibRef
Earlier: A2, A1:
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ICPR12(771-774).
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1302
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Abdulnabi, A.H.,
Wang, G.,
Lu, J.,
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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
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IP(25), No. 8, August 2016, pp. 3919-3930.
IEEE DOI
1608
Computational modeling
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Fan, J.P.[Jian-Ping],
Zhao, T.Y.[Tian-Yi],
Kuang, Z.Z.[Zhen-Zhong],
Zheng, Y.[Yu],
Zhang, J.[Ji],
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Peng, J.Y.[Jin-Ye],
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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],
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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
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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],
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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],
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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:
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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
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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
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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.[Weizhe],
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
Li, Z.[Zheng],
Huang, Y.[Ying],
Chen, D.F.[De-Fang],
Luo, T.[Tianren],
Cai, N.[Ning],
Pan, Z.G.[Zhi-Geng],
Online Knowledge Distillation via Multi-branch Diversity Enhancement,
ACCV20(IV:318-333).
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
Ye, H.J.[Han-Jia],
Lu, S.[Su],
Zhan, D.C.[De-Chuan],
Distilling Cross-Task Knowledge via Relationship Matching,
CVPR20(12393-12402)
IEEE DOI
2008
Task analysis, Neural networks, Training, Knowledge engineering,
Predictive models, Stochastic processes, Temperature measurement
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.,
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
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],
Bulň, 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 .