The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Fully-Adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Classification
Open accessAbstract
Multi-task learning aims to improve generalization performance of multiple prediction tasks by appropriately sharing relevant information across them. In the context of deep neural networks, this idea is often realized by hand-designed network architectures with layers that are shared across tasks and branches that encode task-specific features. However, the space of possible multi-task deep architectures is combinatorially large and often the final architecture is arrived at by manual exploration of this space, which can be both error-prone and tedious. We propose an automatic approach for designing compact multi-task deep learning architectures. Our approach starts with a thin multi-layer network and dynamically widens it in a greedy manner during training. By doing so iteratively, it creates a tree-like deep architecture, on which similar tasks reside in the same branch until at the top layers. Evaluation on person attributes classification tasks involving facial and clothing attributes suggests that the models produced by the proposed method are fast, compact and can closely match or exceed the state-of-the-art accuracy from strong baselines by much more expensive models.
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Cites
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Self-citationMulti-Task Learning with Deep Neural Networks: A Survey
Crawshaw, M. (2020) · arXiv · doi:10.48550/arxiv.2009.09796 · Cited by 428
Multi-task Learning based Pre-trained Language Model for Code Completion
Liu, F., Li, G., Zhao, Y., & Jin, Z. (2020) · Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering · doi:10.1145/3324884.3416591 · Cited by 188
Efficiently Identifying Task Groupings for Multi-Task Learning
Fifty, C., Amid, E., Zhao, Z., Yu, T., Anil, R., & Finn, C. (2021) · arXiv · doi:10.48550/arxiv.2109.04617 · Cited by 125
Detecting 32 Pedestrian Attributes for Autonomous Vehicles
Mordan, T., Cord, M., Perez, P., & Alahi, A. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3107587 · Cited by 36
Metadata
- Authors
- Lu, Y., Kumar, A., Zhai, S., Cheng, Y., Javidi, T., & Feris, R.
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 1131-1140
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2549401308
- Cited by
- 371
- References
- 44
- FWCI
- 18.67
- Type
- conference paper
- Language
- en
- Open access
- green
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- Lu, Y., Kumar, A., Zhai, S., Cheng, Y., Javidi, T., & Feris, R. (2017). Fully-Adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Classification. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2017.126
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