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Multi-task learning has gained popularity due to the advantages it provides with respect to resource usage and performance.
Learning Sparse Networks Using Targeted Dropout
Gomez, A. N.; Zhang, I.; Kamalakara, S. R.; Madaan, D.; Swersky, K.; Gal, Y.; and Hinton, G. E. 2019 · 1905
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Dropout: A Simple Way to Prevent Neural Networks from Overfitting
Srivastava, N.; Hinton, G.; Krizhevsky, A.; Sutskever, I.; and Salakhutdinov, R. 2014 · 1958
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Transfer of Learning by Composing Solutions of Elemental Sequential Tasks
Singh, S. P. 1992 · 1992
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Multitask Learning
Caruana, R. 1997 · 1997
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A Model of Inductive Bias Learning
Baxter, J. 2000 · 2000
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MTI-Net: Multi-Scale Task Interaction Networks for Multi-Task Learning
Vandenhende, S.; Georgoulis, S.; and Van Gool, L. 2020 · 2001
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Multiple-gradient Descent Algorithm (MGDA) for Multiobjective Optimization
Désidéri, J.-A. 2012 · 2012
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Indoor Segmentation and Support Inference from RGBD Images
Silberman, N.; Hoiem, D.; Kohli, P.; and Fergus, R. 2012 · 2012
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How Transferable are Features in Deep Neural Networks?
Yosinski, J.; Clune, J.; Bengio, Y.; and Lipson, H. 2014 · 2014
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Deep Learning Face Attributes in the Wild
Liu, Z.; Luo, P.; Wang, X.; and Tang, X. 2015 · 2015
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The Cityscapes Dataset for Semantic Urban Scene Understanding
Cordts, M.; Omran, M.; Ramos, S.; Rehfeld, T.; Enzweiler, M.; Benenson, R.; Franke, U.; Roth, S.; and Schiele, B. 2016 · 2016
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Deep Residual Learning for Image Recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Cross-Stitch Networks for Multi-Task Learning
Misra, I.; Shrivastava, A.; Gupta, A.; and Hebert, M. 2016 · 2016
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Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
Tajbakhsh, N.; Shin, J. Y.; Gurudu, S. R.; Hurst, R. T.; Kendall, C. B.; Gotway, M. B.; and Liang, J. 2016 · 2016
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SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Badrinarayanan, V.; Kendall, A.; and Cipolla, R. 2017 · 2017
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Mask R-CNN
He, K.; Gkioxari, G.; Dollar, P.; and Girshick, R. 2017 · 2017
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Categorical Reparameterization with Gumbel-Softmax
Jang, E.; Gu, S.; and Poole, B. 2017 · 2017
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Adam: A Method for Stochastic Optimization
Kingma, D. P.; and Ba, J. 2017 · 2017
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Revisiting Multi-Task Learning with ROCK: a Deep Residual Auxiliary Block for Visual Detection
Mordan, T.; Thome, N.; Henaff, G.; and Cord, M. 2018 · 2018
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Multi-Task Learning as Multi-Objective Optimization
Sener, O.; and Koltun, V. 2018 · 2018
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Gradient Adversarial Training of Neural Networks
Sinha, A.; Chen, Z.; Badrinarayanan, V.; and Rabinovich, A. 2018 · 2018
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PAD-Net: Multi-Tasks Guided Prediction-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing
Xu, D.; Ouyang, W.; Wang, X.; and Sebe, N. 2018 · 2018
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Taskonomy: Disentangling Task Transfer Learning
Zamir, A. R.; Sax, A.; Shen, W.; Guibas, L. J.; Malik, J.; and Savarese, S. 2018 · 2018
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Ubernet: Training a Universal Convolutional Neural Network for Low-, Mid-, and High-Level Vision Using Diverse Datasets and Limited Memory
Kokkinos, I. 2017 · 2017
Cited alongside, same era.
Fully-Adaptive Feature Sharing in Multi-Task Networks With Applications in Person Attribute Classification
Lu, Y.; Kumar, A.; Zhai, S.; Cheng, Y.; Javidi, T.; and Feris, R. 2017 · 2017
Cited alongside, same era.
The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables
Maddison, C. J.; Mnih, A.; and Teh, Y. W. 2017 · 2017
Cited alongside, same era.
GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks
Chen, Z.; Badrinarayanan, V.; Lee, C.-Y.; and Rabinovich, A. 2018 · 2018
Cited alongside, same era.
Squeeze-and-Excitation Networks
Hu, J.; Shen, L.; and Sun, G. 2018 · 2018
Cited alongside, same era.
Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
Kendall, A.; Gal, Y.; and Cipolla, R. 2018 · 2018
Cited alongside, same era.
Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights
Mallya, A.; Davis, D.; and Lazebnik, S. 2018 · 2018
Cited alongside, same era.
Zhang, Y.; Wei, Y.; and Yang, Q. 2018 · 2018
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Stochastic Filter Groups for Multi-Task CNNs: Learning Specialist and Generalist Convolution Kernels
Bragman, F. J.; Tanno, R.; Ourselin, S.; Alexander, D. C.; and Cardoso, J. 2019 · 2019
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NDDR-CNN: Layerwise Feature Fusing in Multi-Task CNNs by Neural Discriminative Dimensionality Reduction
Gao, Y.; Ma, J.; Zhao, M.; Liu, W.; and Yuille, A. L. 2019 · 2019
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Pareto Multi-Task Learning
Lin, X.; Zhen, H.-L.; Li, Z.; Zhang, Q.-F.; and Kwong, S. 2019 · 2019
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End-To-End Multi-Task Learning With Attention
Liu, S.; Johns, E.; and Davison, A. J. 2019 · 2019
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Attentive Single-Tasking of Multiple Tasks
Maninis, K.-K.; Radosavovic, I.; and Kokkinos, I. 2019 · 2019
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Many Task Learning With Task Routing
Strezoski, G.; Noord, N. v.; and Worring, M. 2019 · 2019
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Pattern-Affinitive Propagation Across Depth, Surface Normal and Semantic Segmentation
Zhang, Z.; Cui, Z.; Xu, C.; Yan, Y.; Sebe, N.; and Yang, J. 2019 · 2019
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