Cross-attention-map-based regularization for adversarial domain adaptation

doi:10.1016/j.neunet.2021.10.013

CASIA OpenIR > 中科院工业视觉智能装备工程实验室 > 工业智能技术与系统

	Cross-attention-map-based regularization for adversarial domain adaptation
	Jingwei, Li 1,2; Huanjie, Wang1,2 ; Ke, Wu1,2 ; Chengbao, Liu1 ; Jie, Tan1,2
发表期刊	NEURAL NETWORKS
ISSN	0893-6080
	2022
卷号	145 页码:128-138
通讯作者	Jie, Tan(jie.tan@ia.ac.cn)
摘要	In unsupervised domain adaptation (UDA), many efforts are taken to pull the source domain and the target domain closer by adversarial training. Most methods focus on aligning distributions or features between the source domain and the target domain. However, little attention is paid to the interaction between finer-grained levels, such as classes or samples of the two domains. In contrast to UDA, another transfer learning task, i.e., few-shot learning (FSL), takes full advantage of the finer-grained-level alignment. Many FSL methods implement the interaction between samples of support sets and query sets, leading to significant improvements. We wonder whether we can get some inspiration from these methods and bring such ideas of FSL to UDA. To this end, we first take a closer look at the differences between FSL and UDA and bridge the gap between them by high-confidence sample selection (HCSS). Then we propose cross-attention map generation module (CAMGM) to interact samples selected by HCSS. Moreover, we propose a simple but efficient method called cross-attention-map-based regularization (CAMR) to regularize the feature maps generated by the feature extractor. Experiments on three challenging datasets demonstrate that CAMR can bring solid improvements when added to the original objective. More specifically, the proposed CAMR can outperform original methods by 1% to 2% in most tasks without bells and whistles. (C) 2021 Elsevier Ltd. All rights reserved.
关键词	Domain adaptation Few-shot learning Attention mechanism Contrastive learning
DOI	10.1016/j.neunet.2021.10.013
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China under People's Republic of China[U1801263] ; National Natural Science Foundation of China under People's Republic of China[U1701262]
项目资助者	National Natural Science Foundation of China under People's Republic of China
WOS研究方向	Computer Science ; Neurosciences & Neurology
WOS类目	Computer Science, Artificial Intelligence ; Neurosciences
WOS记录号	WOS:000717665500007
出版者	PERGAMON-ELSEVIER SCIENCE LTD
七大方向——子方向分类	机器学习
引用统计	被引频次：8[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/46513
专题	中科院工业视觉智能装备工程实验室_工业智能技术与系统
通讯作者	Jie, Tan
作者单位	1.Chinese Acad Sci, Inst Automat, 95 East Zhongguancun Rd, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Jingwei, Li,Huanjie, Wang,Ke, Wu,et al. Cross-attention-map-based regularization for adversarial domain adaptation[J]. NEURAL NETWORKS,2022,145:128-138.
APA	Jingwei, Li,Huanjie, Wang,Ke, Wu,Chengbao, Liu,&Jie, Tan.(2022).Cross-attention-map-based regularization for adversarial domain adaptation.NEURAL NETWORKS,145,128-138.
MLA	Jingwei, Li,et al."Cross-attention-map-based regularization for adversarial domain adaptation".NEURAL NETWORKS 145(2022):128-138.