Knowledge Commons of Institute of Automation,CAS
Exploring Explicitly Disentangled Features for Domain Generalization | |
Li, Jingwei1,2; Li, Yuan1,2; Wang, Huanjie1,2; Liu, Chengbao1; Tan, Jie1,2 | |
发表期刊 | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY |
ISSN | 1051-8215 |
2023-11-01 | |
卷号 | 33期号:11页码:6360-6373 |
摘要 | Domain generalization (DG) is a challenging task that aims to train a robust model with only labeled source data and can generalize well on unseen target data. The domain gap between the source and target data may degrade the performance. A plethora of methods resort to obtaining domain-invariant features to overcome the difficulties. However, these methods require sophisticated network designs or training strategies, causing inefficiency and complexity. In this paper, we first analyze and reclassify the features into two categories, i.e., implicitly disentangled ones and explicitly disentangled counterparts. Since we aim to design a generic algorithm for DG to alleviate the problems mentioned above, we focus more on the explicitly disentangled features due to their simplicity and interpretability. We find out that the shape features of images are simple and elegant choices based on our analysis. We extract the shape features from two aspects. In the aspect of networks, we propose Multi-Scale Amplitude Mixing (MSAM) to strengthen shape features at different layers of the network by Fourier transform. In the aspect of inputs, we propose a new data augmentation method called Random Shape Warping (RSW) to facilitate the model to concentrate more on the global structures of the objects. RSW randomly distorts the local parts of the images and keeps the global structures unchanged, which can further improve the robustness of the model. Our methods are simple yet efficient and can be conveniently used as plug-and-play modules. They can outperform state-of-the-art (SOTA) methods without bells and whistles. |
关键词 | Domain generalization feature disentanglement Fourier transform data augmentation |
DOI | 10.1109/TCSVT.2023.3269534 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key Research and Development Program of China[2022YFB3304602] ; National Natural Science Foundation of China[62003344] |
项目资助者 | National Key Research and Development Program of China ; National Natural Science Foundation of China |
WOS研究方向 | Engineering |
WOS类目 | Engineering, Electrical & Electronic |
WOS记录号 | WOS:001093434100012 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
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/173211/54434 |
专题 | 中国科学院工业视觉智能装备工程实验室 |
通讯作者 | Tan, Jie |
作者单位 | 1.Chinese Acad Sci, Inst Automat, Beijing 100080, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, 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 | Li, Jingwei,Li, Yuan,Wang, Huanjie,et al. Exploring Explicitly Disentangled Features for Domain Generalization[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2023,33(11):6360-6373. |
APA | Li, Jingwei,Li, Yuan,Wang, Huanjie,Liu, Chengbao,&Tan, Jie.(2023).Exploring Explicitly Disentangled Features for Domain Generalization.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,33(11),6360-6373. |
MLA | Li, Jingwei,et al."Exploring Explicitly Disentangled Features for Domain Generalization".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 33.11(2023):6360-6373. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
paper2.pdf(2432KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论