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Adaptive multi-task learning for fine-grained categorization
Gang Sun1,2; Yanyun Chen1; Xuehui Liu1; Enhua Wu1,3
Conference NameIEEE International Conference on Image Processing, ICIP 2015
Source Publication2015 IEEE International Conference on Image Processing (ICIP)
Conference Date27-30 Sept. 2015
Conference PlaceQuebec City, QC, Canada
Author of SourceIEEE Computer Society

Multi-task learning has been proposed to improve the generalization performance by learning multiple tasks jointly. One challenge for this learning paradigm is to effectively seek the shared information across multiple tasks. In this paper, we propose a novel multi-task learning method to adaptively share information. Unlike many existing multi-task learning methods which impose strong assumptions on task related-ness, our method captures the relationships among tasks and identifies the disparities of each task simultaneously, thus can flexibly exploit the shared information. Moreover, we apply it to fine-grained categorization problem, which usually suffers from the difficulties of insufficient training data and high inter-class similarity. The experimental results on two widely used datasets show the superiority of our method compared with some state-of-the-art methods. © 2015 IEEE.

Indexed BySCI
WOS Research AreaEngineering ; Imaging Science & Photographic Technology
WOS SubjectEngineering, Electrical & Electronic ; Imaging Science & Photographic Technology
WOS IDWOS:000371977801022
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Cited Times [WOS]:4   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
Faculty of Science and Technology
Affiliation1.State Key Lab. of Computer Science, Inst. of Software, Chinese Academy of Sciences, China
2.University of Chinese Academy of Sciences, China;
3.University of Macau, China
Recommended Citation
GB/T 7714
Gang Sun,Yanyun Chen,Xuehui Liu,et al. Adaptive multi-task learning for fine-grained categorization[C]//IEEE Computer Society,2015:996-1000.
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