中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Where and How to Transfer: Knowledge Aggregation-Induced Transferability Perception for Unsupervised Domain Adaptation

文献类型:期刊论文

作者Dong JH(董家华)2,3,4; Cong Y(丛杨)2,3; Sun G(孙干)2,3; Fang, Zhen5; Ding ZM(丁正明)1
刊名IEEE Transactions on Pattern Analysis and Machine Intelligence
出版日期2021
页码1-17
关键词Transfer Learning Unsupervised Domain Adaptation Semantic Segmentation Medical Lesions Diagnosis
ISSN号0162-8828
产权排序1
英文摘要

Unsupervised domain adaptation without accessing expensive annotation processes of target data has achieved remarkable successes in semantic segmentation. However, most existing state-of-the-art methods cannot explore whether semantic representations across domains are transferable or not, which may result in the negative transfer brought by irrelevant knowledge. To tackle this challenge, in this paper, we develop a novel Knowledge Aggregation-induced Transferability Perception (KATP) for unsupervised domain adaptation, which is a pioneering attempt to distinguish transferable or untransferable knowledge across domains. Specifically, the KATP module is designed to quantify which semantic knowledge across domains is transferable, by incorporating transferability information propagation from global category-wise prototypes. Based on KATP, we design a novel KATP Adaptation Network (KATPAN) to determine where and how to transfer. The KATPAN contains a transferable appearance translation module T_A() and a transferable representation augmentation module T_R(), where both modules construct a virtuous circle of performance promotion. T_A() develops a transferability-aware information bottleneck to highlight where to adapt transferable visual characterizations and modality information; T_R() explores how to augment transferable representations while abandoning untransferable information, and promotes the translation performance of T_A() in return. Experiments on several representative datasets and a medical dataset support the state-of-the-art performance of our model.

语种英语
资助机构National Key Research and Development Program of China under Grant 2019YFB1310300 ; National Nature Science Foundation of China under Grant 61821005, and Grant 62003336 ; National Postdoctoral Innovative Talents Support Program (BX20200353)
源URL[http://ir.sia.cn/handle/173321/30084]  
专题沈阳自动化研究所_机器人学研究室
通讯作者Cong Y(丛杨)
作者单位1.Department of Computer Science, Tulane University, New Orleans, LA 70118, USA.
2.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
3.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
4.University of Chinese Academy of Sciences, Beijing, 100049, China
5.Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW 2007, Australia
推荐引用方式
GB/T 7714
Dong JH,Cong Y,Sun G,et al. Where and How to Transfer: Knowledge Aggregation-Induced Transferability Perception for Unsupervised Domain Adaptation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2021:1-17.
APA Dong JH,Cong Y,Sun G,Fang, Zhen,&Ding ZM.(2021).Where and How to Transfer: Knowledge Aggregation-Induced Transferability Perception for Unsupervised Domain Adaptation.IEEE Transactions on Pattern Analysis and Machine Intelligence,1-17.
MLA Dong JH,et al."Where and How to Transfer: Knowledge Aggregation-Induced Transferability Perception for Unsupervised Domain Adaptation".IEEE Transactions on Pattern Analysis and Machine Intelligence (2021):1-17.

入库方式: OAI收割

来源:沈阳自动化研究所

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