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Training multiple tasks jointly in one deep network yields reduced latency during inference and better performance over the single-task counterpart by sharing certain layers of a network.
The need for biases in learning generalizations
Mitchell, T. M · 1980
Earlier work this paper cites.
Multitask learning
Caruana, R · 1997
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
Earlier work this paper cites.
Visualizing and understanding convolutional networks
Zeiler, M. D. and Fergus, R · 2014
Earlier work this paper cites.
Low resource dependency parsing: Cross-lingual parameter sharing in a neural network parser
Duong, L., Cohn, T., Bird, S., and Cook, P · 2015
Earlier work this paper cites.
Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
Earlier work this paper cites.
Learning multiple tasks with deep relationship networks
Long, M. and Wang, J · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2015
Earlier work this paper cites.
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
Generalizing pooling functions in convolutional neural networks: Mixed, gated, and tree
Lee, C.-Y., Gallagher, P. W., and Tu, Z · 2016
Earlier work this paper cites.
Cross-stitch networks for multi-task learning
Misra, I., Shrivastava, A., Gupta, A., and Hebert, M · 2016
Earlier work this paper cites.
Moon: A mixed objective optimization network for the recognition of facial attributes
Rudd, E. M., Günther, M., and Boult, T. E · 2016
Earlier work this paper cites.
Walk and learn: Facial attribute representation learning from egocentric video and contextual data
Wang, J., Cheng, Y., and Schmidt Feris, R · 2016
Earlier work this paper cites.
Network dissection: Quantifying interpretability of deep visual representations
Bau, D., Zhou, B., Khosla, A., Oliva, A., and Torralba, A · 2017
Earlier work this paper cites.
Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A. L · 2017
Earlier work this paper cites.
Attributes for improved attributes: A multi-task network utilizing implicit and explicit relationships for facial attribute classification
Hand, E. M. and Chellappa, R · 2017
Cited alongside, same era.
Categorical reparameterization with gumbel-softmax
Jang, E., Gu, S., and Poole, B · 2017
Cited alongside, same era.
Ubernet: Training a universal convolutional neural network for low-, mid-, and high-level vision using diverse datasets and limited memory
Kokkinos, I · 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
Cited alongside, same era.
An overview of multi-task learning in deep neural networks
Ruder, S · 2017
Cited alongside, same era.
Routing networks: Adaptive selection of non-linear functions for multi-task learning
Rosenbaum, C., Klinger, T., and Riemer, M · 2018
Later among the works it cites.
Multi-task learning as multi-objective optimization
Sener, O. and Koltun, V · 2018
Later among the works it cites.
Convolutional networks with adaptive inference graphs
Veit, A. and Belongie, S · 2018
Later among the works it cites.
Transfer learning with neural automl
Wong, C., Houlsby, N., Lu, Y., and Gesmundo, A · 2018
Later among the works it cites.
Taskonomy: Disentangling task transfer learning
Zamir, A. R., Sax, A., Shen, W., Guibas, L. J., Malik, J., and Savarese, S · 2018
Later among the works it cites.
Learning transferable architectures for scalable image recognition
Zoph, B., Vasudevan, V., Shlens, J., and Le, Q. V · 2018
Later among the works it cites.
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Sluice networks: Learning what to share between loosely related tasks
Ruder, S., Bingel, J., Augenstein, I., and Søgaard, A · 2017
Cited alongside, same era.
Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Shazeer, N., Mirhoseini, A., Maziarz, K., Davis, A., Le, Q., Hinton, G., and Dean, J · 2017
Cited alongside, same era.
Neural architecture search with reinforcement learning
Zoph, B. and Le, Q. V · 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
Cited alongside, same era.
Dynamic task prioritization for multitask learning
Guo, M., Haque, A., Huang, D.-A., Yeung, S., and Fei-Fei, L · 2018
Cited alongside, same era.
Gnas: A greedy neural architecture search method for multi-attribute learning
Huang, S., Li, X., Cheng, Z.-Q., Zhang, Z., and Hauptmann, A · 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
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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
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Gumbel-matrix routing for flexible multi-task learning
Maziarz, K., Kokiopoulou, E., Gesmundo, A., Sbaiz, L., Bartok, G., and Berent, J · 2019
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Continual and multi-task architecture search
Pasunuru, R. and Bansal, M · 2019
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Latent multi-task architecture learning
Ruder, S., Bingel, J., Augenstein, I., and Søgaard, A · 2019
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Evaluating the search phase of neural architecture search
Sciuto, C., Yu, K., Jaggi, M., Musat, C., and Salzmann, M · 2019
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Meta architecture search
Shaw, A., Wei, W., Liu, W., Song, L., and Dai, B · 2019
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Which tasks should be learned together in multi-task learning?
Standley, T., Zamir, A. R., Chen, D., Guibas, L., Malik, J., and Savarese, S · 2019
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Adashare: Learning what to share for efficient deep multi-task learning
Sun, X., Panda, R., and Feris, R · 2019
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Branched multi-task networks: deciding what layers to share
Vandenhende, S., De Brabandere, B., and Van Gool, L · 2019
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Snas: stochastic neural architecture search
Xie, S., Zheng, H., Liu, C., and Lin, L · 2019
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