FeatsFlow: Traceable representation learning based on normalizing flows
文献类型：期刊论文
| 作者 | Zhang, Wenwen2; Pei, Zhao2; Wang, Fei-Yue1,3,4
|
| 刊名 | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
 |
| 出版日期 | 2023-11-01 |
| 卷号 | 126页码:13 |
| ISSN号 | 0952-1976 |
| 关键词 | Representation learning Distribution transformation Traceable features Normalizing flows |
| DOI | 10.1016/j.engappai.2023.107151 |
| 通讯作者 | Zhang, Wenwen(2021136@snnu.edu.cn) |
| 英文摘要 | This paper studies effective traceable feature representation learning in the view of distribution transformation, termed FeatsFlow, by proposing a distribution-aware learning framework combining the discriminating model with a normalizing flow-based model. The process can be regarded as a series of feature distribution transformations, from the input images to the expected results. Focusing on the learned representation of the target model, we take full advantage of the invertible nature of normalizing flows and learn the practical and traceable feature representation for target goals. Considering that it is difficult to model the traceable process for feature extraction, we propose an effective model by combining a general discriminating model with normalizing flows for traceable feature extraction. The normalizing flows module is added to the original model in a plug-in mode, which is convenient to make it available for effective and traceable feature learning. Thus we can obtain an effective and traceable representation distribution. Extensive experiments are conducted on our proposed representation learning model for the image classification task, and the experimental results illustrate that our proposed model is adequate for traceable representation learning. The most important is that we present a distribution-aware representation learning approach, which makes it possible to conduct and understand feature representation learning at the feature level. |
| 资助项目 | Natural Science Foundation for Young Scientists in Shaanxi Province of China[2023-JC-QN-0729] ; Fundamental Research Funds for the Central Universities[GK202207008] |
| WOS研究方向 | Automation & Control Systems ; Computer Science ; Engineering |
| 语种 | 英语 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| WOS记录号 | WOS:001081735900001 |
| 资助机构 | Natural Science Foundation for Young Scientists in Shaanxi Province of China ; Fundamental Research Funds for the Central Universities |
| 源URL | [http://ir.ia.ac.cn/handle/173211/52968]  |
| 专题 | 多模态人工智能系统全国重点实验室 |
| 通讯作者 | Zhang, Wenwen |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Shaanxi Normal Univ, Sch Comp Sci, Xian 710119, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 4.Macau Univ Sci & Technol, Inst Syst Engn, Macau 999078, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Wenwen,Pei, Zhao,Wang, Fei-Yue. FeatsFlow: Traceable representation learning based on normalizing flows[J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE,2023,126:13. |
| APA | Zhang, Wenwen,Pei, Zhao,&Wang, Fei-Yue.(2023).FeatsFlow: Traceable representation learning based on normalizing flows.ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE,126,13. |
| MLA | Zhang, Wenwen,et al."FeatsFlow: Traceable representation learning based on normalizing flows".ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 126(2023):13. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
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。