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Learning discriminative task-specific features simultaneously for multiple distinct tasks is a fundamental problem in multi-task learning.
Adaptive mixtures of local experts
Robert A Jacobs, Michael I Jordan, Steven J Nowlan, and Geoffrey E Hinton · 1991
Earlier work this paper cites.
Learning piecewise control strategies in a modular neural network architecture
Robert A Jacobs and Michael I Jordan · 1993
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Hierarchical mixtures of experts and the em algorithm
Michael I Jordan and Robert A Jacobs · 1994
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2010
Earlier work this paper cites.
Indoor segmentation and support inference from rgbd images
Nathan Silberman, Derek Hoiem, Pushmeet Kohli, and Rob Fergus · 2012
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Learning factored representations in a deep mixture of experts
David Eigen, Marc’Aurelio Ranzato, and Ilya Sutskever · 2013
Earlier work this paper cites.
Detect what you can: Detecting and representing objects using holistic models and body parts
Xianjie Chen, Roozbeh Mottaghi, Xiaobai Liu, Sanja Fidler, Raquel Urtasun, and Alan Yuille · 2014
Earlier work this paper cites.
Cross-stitch networks for multi-task learning
Ishan Misra, Abhinav Shrivastava, Abhinav Gupta, and Martial Hebert · 2016
Earlier work this paper cites.
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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Feature pyramid networks for object detection
Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Fully-adaptive feature sharing in multi-task networks with applications in person attribute classification
Yongxi Lu, Abhishek Kumar, Shuangfei Zhai, Yu Cheng, Tara Javidi, and Rogerio Feris · 2017
Earlier work this paper cites.
Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean · 2017
Earlier work this paper cites.
Multi-task learning using uncertainty to weigh losses for scene geometry and semantics
Alex Kendall, Yarin Gal, and Roberto Cipolla · 2018
Earlier work this paper cites.
Pad-net: Multi-tasks guided prediction-and-distillation network for simultaneous depth estimation and scene parsing
Dan Xu, Wanli Ouyang, Xiaogang Wang, and Nicu Sebe · 2018
Earlier work this paper cites.
Taskonomy: Disentangling task transfer learning
Amir R Zamir, Alexander Sax, William Shen, Leonidas J Guibas, Jitendra Malik, and Silvio Savarese · 2018
Earlier work this paper cites.
Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks
Zhao Chen, Vijay Badrinarayanan, Chen-Yu Lee, and Andrew Rabinovich · 2018
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Modeling task relationships in multi-task learning with multi-gate mixture-of-experts
Jiaqi Ma, Zhe Zhao, Xinyang Yi, Jilin Chen, Lichan Hong, and Ed H Chi · 2018
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Attentive single-tasking of multiple tasks
Kevis-Kokitsi Maninis, Ilija Radosavovic, and Iasonas Kokkinos · 2019
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End-to-end multi-task learning with attention
Shikun Liu, Edward Johns, and Andrew J Davison · 2019
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Nddr-cnn: Layerwise feature fusing in multi-task cnns by neural discriminative dimensionality reduction
Yuan Gao, Jiayi Ma, Mingbo Zhao, Wei Liu, and Alan L Yuille · 2019
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Pattern-affinitive propagation across depth, surface normal and semantic segmentation
Multi-task learning for dense prediction tasks: A survey
Simon Vandenhende, Stamatios Georgoulis, Wouter Van Gansbeke, Marc Proesmans, Dengxin Dai, and Luc Van Gool · 2021
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Three ways to improve semantic segmentation with self-supervised depth estimation
Lukas Hoyer, Dengxin Dai, Yuhua Chen, Adrian Koring, Suman Saha, and Luc Van Gool · 2021
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Conflict-averse gradient descent for multi-task learning
Bo Liu, Xingchao Liu, Xiaojie Jin, Peter Stone, and Qiang Liu · 2021
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Automtl: A programming framework for automating efficient multi-task learning
Lijun Zhang, Xiao Liu, and Hui Guan · 2021
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Transfer vision patterns for multi-task pixel learning
Xiaoya Zhang, Ling Zhou, Yong Li, Zhen Cui, Jin Xie, and Jian Yang · 2021
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Gshard: Scaling giant models with conditional computation and automatic sharding
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Zhenyu Zhang, Zhen Cui, Chunyan Xu, Yan Yan, Nicu Sebe, and Jian Yang · 2019
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Mti-net: Multi-scale task interaction networks for multi-task learning
Simon Vandenhende, Stamatios Georgoulis, and Luc Van Gool · 2020
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Robust learning through cross-task consistency
Amir R Zamir, Alexander Sax, Nikhil Cheerla, Rohan Suri, Zhangjie Cao, Jitendra Malik, and Leonidas J Guibas · 2020
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Just pick a sign: Optimizing deep multitask models with gradient sign dropout
Zhao Chen, Jiquan Ngiam, Yanping Huang, Thang Luong, Henrik Kretzschmar, Yuning Chai, and Dragomir Anguelov · 2020
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Gradient vaccine: Investigating and improving multi-task optimization in massively multilingual models
Zirui Wang, Yulia Tsvetkov, Orhan Firat, and Yuan Cao · 2020
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Gradient surgery for multi-task learning
Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, and Chelsea Finn · 2020
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Mtl-nas: Task-agnostic neural architecture search towards general-purpose multi-task learning
Yuan Gao, Haoping Bai, Zequn Jie, Jiayi Ma, Kui Jia, and Wei Liu · 2020
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Dmitry Lepikhin, HyoukJoong Lee, Yuanzhong Xu, Dehao Chen, Orhan Firat, Yanping Huang, Maxim Krikun, Noam Shazeer, and Zhifeng Chen · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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Inverted pyramid multi-task transformer for dense scene understanding
Hanrong Ye and Dan Xu · 2022
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Multimae: Multi-modal multi-task masked autoencoders
Roman Bachmann, David Mizrahi, Andrei Atanov, and Amir Zamir · 2022
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Learning multiple dense prediction tasks from partially annotated data
Wei-Hong Li, Xialei Liu, and Hakan Bilen · 2022
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Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity
William Fedus, Barret Zoph, and Noam Shazeer · 2022
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M3vit: Mixture-of-experts vision transformer for efficient multi-task learning with model-accelerator co-design
Hanxue Liang, Zhiwen Fan, Rishov Sarkar, Ziyu Jiang, Tianlong Chen, Kai Zou, Yu Cheng, Cong Hao, and Zhangyang Wang · 2022
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Mod-squad: Designing mixture of experts as modular multi-task learners
Zitian Chen, Yikang Shen, Mingyu Ding, Zhenfang Chen, Hengshuang Zhao, Erik Learned-Miller, and Chuang Gan · 2022
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Composite learning for robust and effective dense predictions
Menelaos Kanakis, Thomas E Huang, David Brüggemann, Fisher Yu, and Luc Van Gool · 2023
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Contrastive multi-task dense prediction
Siwei Yang, Hanrong Ye, and Dan Xu · 2023
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Taskprompter: Spatial-channel multi-task prompting for dense scene understanding
Hanrong Ye and Dan Xu · 2023
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