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Multi-task learning (MTL) is an efficient solution to solve multiple tasks simultaneously in order to get better speed and performance than handling each single-task in turn.
A bayesian/information theoretic model of learning to learn via multiple task sampling
Jonathan Baxter · 1997
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Indoor segmentation and support inference from rgbd images
Nathan Silberman, Derek Hoiem, Pushmeet Kohli, and Rob Fergus · 2012
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Low resource dependency parsing: Cross-lingual parameter sharing in a neural network parser
Long Duong, Trevor Cohn, Steven Bird, and Paul Cook · 2015
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Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture
David Eigen and Rob Fergus · 2015
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Sun rgb-d: A rgb-d scene understanding benchmark suite
Shuran Song, Samuel P Lichtenberg, and Jianxiong Xiao · 2015
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Training deeper convolutional networks with deep supervision
Liwei Wang, Chen-Yu Lee, Zhuowen Tu, and Svetlana Lazebnik · 2015
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Understanding real world indoor scenes with synthetic data
Ankur Handa, Viorica Patraucean, Vijay Badrinarayanan, Simon Stent, and Roberto Cipolla · 2016
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Learning depth from single monocular images using deep convolutional neural fields
Fayao Liu, Chunhua Shen, Guosheng Lin, and Ian D Reid · 2016
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Cross-stitch networks for multi-task learning
Ishan Misra, Abhinav Shrivastava, Abhinav Gupta, and Martial Hebert · 2016
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Joint semantic segmentation and depth estimation with deep convolutional networks
Arsalan Mousavian, Hamed Pirsiavash, and Jana Košecká · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna · 2016
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Trace norm regularised deep multi-task learning
Yongxin Yang and Timothy M Hospedales · 2016
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Estimating depth from monocular images as classification using deep fully convolutional residual networks
Yuanzhouhan Cao, Zifeng Wu, and Chunhua Shen · 2017
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Deformable convolutional networks
Jifeng Dai, Haozhi Qi, Yuwen Xiong, Yi Li, Guodong Zhang, Han Hu, and Yichen Wei · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens van der Maaten, and Kilian Q. Weinberger · 2017
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Ubernet: Training a universal convolutional neural network for low-, mid-, and high-level vision using diverse datasets and limited memory
Iasonas Kokkinos · 2017
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Learning multiple tasks with multilinear relationship networks
Mingsheng Long, Zhangjie Cao, Jianmin Wang, and S Yu Philip · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Deep multi-task representation learning: A tensor factorisation approach
Yongxin Yang and Timothy Hospedales · 2017
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Extragradient method in optimization: Convergence and complexity
Trong Phong Nguyen, Edouard Pauwels, Emile Richard, and Bruce W Suter · 2018
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Mutual learning to adapt for joint human parsing and pose estimation
Xuecheng Nie, Jiashi Feng, and Shuicheng Yan · 2018
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Efficient neural architecture search via parameter sharing
Hieu Pham, Melody Y Guan, Barret Zoph, Quoc V Le, and Jeff Dean · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
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Multi-task learning as multi-objective optimization
Ozan Sener and Vladlen Koltun · 2018
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Pad-net: Multi-tasks guided prediction-and-distillation network for simultaneous depth estimation and scene parsing
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Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia · 2017
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Efficient architecture search by network transformation
Han Cai, Tianyao Chen, Weinan Zhang, Yong Yu, and Jun Wang · 2018
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Deep ordinal regression network for monocular depth estimation
Huan Fu, Mingming Gong, Chaohui Wang, Kayhan Batmanghelich, and Dacheng Tao · 2018
Cited alongside, same era.
Dynamic task prioritization for multitask learning
Michelle Guo, Albert Haque, De-An Huang, Serena Yeung, and Li Fei-Fei · 2018
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Multi-task learning using uncertainty to weigh losses for scene geometry and semantics
Alex Kendall, Yarin Gal, and Roberto Cipolla · 2018
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Deep attention-based classification network for robust depth prediction
Ruibo Li, Ke Xian, Chunhua Shen, Zhiguo Cao, Hao Lu, and Lingxiao Hang · 2018
Cited alongside, same era.
Hierarchical representations for efficient architecture search
Hanxiao Liu, Karen Simonyan, Oriol Vinyals, Chrisantha Fernando, and Koray Kavukcuoglu · 2018
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Dan Xu, Wanli Ouyang, Xiaogang Wang, and Nicu Sebe · 2018
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Ocnet: Object context network for scene parsing
Yuhui Yuan and Jingdong Wang · 2018
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Joint task-recursive learning for semantic segmentation and depth estimation
Zhenyu Zhang, Zhen Cui, Chunyan Xu, Zequn Jie, Xiang Li, and Jian Yang · 2018
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Practical block-wise neural network architecture generation
Zhao Zhong, Junjie Yan, Wei Wu, Jing Shao, and Cheng-Lin Liu · 2018
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Multi-task learning with multi-view attention for answer selection and knowledge base question answering
Yang Deng, Yuexiang Xie, Yaliang Li, Min Yang, Nan Du, Wei Fan, Kai Lei, and Ying Shen · 2019
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Structured knowledge distillation for semantic segmentation
Yifan Liu, Ke Chen, Chris Liu, Zengchang Qin, Zhenbo Luo, and Jingdong Wang · 2019
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Fast neural architecture search of compact semantic segmentation models via auxiliary cells
Vladimir Nekrasov, Hao Chen, Chunhua Shen, and Ian Reid · 2019
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A hierarchical multi-task approach for learning embeddings from semantic tasks
Victor Sanh, Thomas Wolf, and Sebastian Ruder · 2019
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