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Adversarial Learning Guided Task Relatedness Refinement for Multi-Task Deep Learning

文献类型：期刊论文

作者Fang, Yuchun1; Cai, Sirui1; Cao, Yiting1; Li, Zhengchen1; Zhang, Zhaoxiang2,3
刊名IEEE TRANSACTIONS ON MULTIMEDIA
出版日期2023
卷号25页码:6946-6957
关键词Index Terms-Multi-task learning deep learning task relatedness
ISSN号1520-9210
DOI10.1109/TMM.2022.3216460
通讯作者Fang, Yuchun(ycfang@shu.edu.cn)
英文摘要In machine learning, the relatedness across multiple tasks is usually complex and entangled. Due to dataset bias, the relatedness among tasks might be distorted and mislead the training of the models with solid learning ability, such as the multi-task neural networks. In this paper, we propose the idea of Relatedness Refinement Multi-Task Learning (RRMTDL) by introducing adversarial learning in the multi-task deep neural network to tackle the problem. The RRMTDL deep learning model restrains the misleading relatedness task by adversarial training and extracts information sharing across tasks with valuable relatedness. With RRMTDL, multi-task deep learning can enhance the task-specific representation for the major tasks by excluding the misleading relatedness. We design tests with various combinations of task-relatedness to validate the proposed model. Experimental results show that the RRMTDL model can effectively refine the task relatedness and prominently outperform other multi-task deep learning models in datasets with entangled task labels.
资助项目National Natural Science Foundation of China[61976132] ; National Natural Science Foundation of China[61991411] ; National Natural Science Foundation of China[U1811461] ; Natural Science Foundation of Shanghai[19ZR1419200]
WOS研究方向Computer Science ; Telecommunications
语种英语
WOS记录号WOS:001102654000017
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Natural Science Foundation of China ; Natural Science Foundation of Shanghai
源URL[http://ir.ia.ac.cn/handle/173211/55155]  
专题多模态人工智能系统全国重点实验室
通讯作者Fang, Yuchun
作者单位1.Shanghai Univ, Sch Comp Engn & Sci, Shanghai 200444, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
3.HKISI CAS, Ctr Artificial Intelligence & Robot, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Fang, Yuchun,Cai, Sirui,Cao, Yiting,et al. Adversarial Learning Guided Task Relatedness Refinement for Multi-Task Deep Learning[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2023,25:6946-6957.
APA Fang, Yuchun,Cai, Sirui,Cao, Yiting,Li, Zhengchen,&Zhang, Zhaoxiang.(2023).Adversarial Learning Guided Task Relatedness Refinement for Multi-Task Deep Learning.IEEE TRANSACTIONS ON MULTIMEDIA,25,6946-6957.
MLA Fang, Yuchun,et al."Adversarial Learning Guided Task Relatedness Refinement for Multi-Task Deep Learning".IEEE TRANSACTIONS ON MULTIMEDIA 25(2023):6946-6957.

入库方式： OAI收割

来源：自动化研究所

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