Knowledge Commons of Institute of Automation,CAS
Adversarial Learning Guided Task Relatedness Refinement for Multi-Task Deep Learning | |
Fang, Yuchun1; Cai, Sirui1; Cao, Yiting1; Li, Zhengchen1; Zhang, Zhaoxiang2,3 | |
发表期刊 | IEEE TRANSACTIONS ON MULTIMEDIA |
ISSN | 1520-9210 |
2023 | |
卷号 | 25页码:6946-6957 |
通讯作者 | Fang, Yuchun(ycfang@shu.edu.cn) |
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. |
关键词 | Index Terms-Multi-task learning deep learning task relatedness |
DOI | 10.1109/TMM.2022.3216460 |
收录类别 | SCI |
语种 | 英语 |
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] |
项目资助者 | National Natural Science Foundation of China ; Natural Science Foundation of Shanghai |
WOS研究方向 | Computer Science ; Telecommunications |
WOS类目 | Computer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications |
WOS记录号 | WOS:001102654000017 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/55156 |
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. |
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