Abstract
Multi-task learning has been widely adopted in many computer vision tasks to improve overall computation efficiency or boost the performance of individual tasks, under the assumption that those tasks are correlated and complementary to each other. However, the relationships between the tasks are complicated in practice, especially when the number of involved tasks scales up. When two tasks are of weak relevance, they may compete or even distract each other during joint training of shared parameters, and as a consequence undermine the learning of all the tasks. This will raise destructive interference which decreases learning efficiency of shared parameters and lead to low quality loss local optimum w.r.t. shared parameters. To address the this problem, we propose a general modulation module, which can be inserted into any convolutional neural network architecture, to encourage the coupling and feature sharing of relevant tasks while disentangling the learning of irrelevant tasks with minor parameters addition. Equipped with this module, gradient directions from different tasks can be enforced to be consistent for those shared parameters, which benefits multi-task joint training. The module is end-to-end learnable without ad-hoc design for specific tasks, and can naturally handle many tasks at the same time. We apply our approach on two retrieval tasks, face retrieval on the CelebA dataset [12] and product retrieval on the UT-Zappos50K dataset [34, 35], and demonstrate its advantage over other multi-task learning methods in both accuracy and storage efficiency.
Original language | English (US) |
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Title of host publication | Computer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings |
Editors | Martial Hebert, Vittorio Ferrari, Cristian Sminchisescu, Yair Weiss |
Publisher | Springer Verlag |
Pages | 415-432 |
Number of pages | 18 |
ISBN (Print) | 9783030012458 |
DOIs | |
State | Published - 2018 |
Event | 15th European Conference on Computer Vision, ECCV 2018 - Munich, Germany Duration: Sep 8 2018 → Sep 14 2018 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 11205 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Other
Other | 15th European Conference on Computer Vision, ECCV 2018 |
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Country/Territory | Germany |
City | Munich |
Period | 9/8/18 → 9/14/18 |
Funding
Acknowledgements. This work was supported in part by National Science Foundation grant IIS-1217302, IIS-1619078, the Army Research Office ARO W911NF-16-1-0138, and Adobe Collaboration Funding.
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science