Knowledge Commons of Institute of Automation,CAS
Cross-attention-map-based regularization for adversarial domain adaptation | |
Jingwei, Li1,2; 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 |
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 |
国重实验室规划方向分类 | 虚实融合与迁移学习 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/46513 |
专题 | 中国科学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. |
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