KMS Of Academy of mathematics and systems sciences, CAS
| Study of Restrained Network Structures for Wasserstein Generative Adversarial Networks (WGANs) on Numeric Data Augmentation | |
| Wang, Wei1,2; Wang, Chuang1; Cui, Tao3; Li, Yue1,2 | |
| 2020 | |
| Source Publication | IEEE ACCESS
 |
| ISSN | 2169-3536 |
| Volume | 8Pages:89812-89821 |
| Abstract | Some recent studies have suggested using Generative Adversarial Network (GAN) for numeric data over-sampling, which is to generate data for completing the imbalanced numeric data. Compared with the conventional over-sampling methods, taken SMOTE as an example, the recently-proposed GAN schemes fail to generate distinguishable augmentation results for classifiers. In this paper, we discuss the reason for such failures, based on which we further study the restrained conditions between $G$ and $D$ theoretically, and propose a quantitative indicator of the restrained structure, called Similarity of the Restrained Condition (SRC) to measure the restrained conditions. Practically, we propose several candidate solutions, which are isomorphic (IWGAN) mirror (MWGAN) and self-symmetric WGAN (SWGAN) for restrained conditions. Besides, the restrained WGANs enhance the classification performance in AUC on five classifiers compared with the original data as the baseline, conventional SMOTE, and other GANs add up to 20 groups of experiments in four datasets. The restrained WGANs outperform all others in 17/20 groups, among which IWGAN accounted for 15/17 groups and the SRC is an effective measure in evaluating the restraints so that further GAN structures with |
| Keyword | Gallium nitride Generative adversarial networks Tensile stress Generators Indexes Training Numerical models Restrained network structures generative adversarial network numeric data augmentation |
| DOI | 10.1109/ACCESS.2020.2993839 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | Tianjin Natural Science Foundations[17JCYBJC23000] ; National Key Research and Development Program of China[2018hjyzkfkt-002] ; National Key Research and Development Program of China[2016YFB0201304] ; Fundamental Research Funds for the Central Universities, Nankai University[070/63191114] |
| WOS Research Area | Computer Science ; Engineering ; Telecommunications |
| WOS Subject | Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications |
| WOS ID | WOS:000538727700078 |
| Publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.amss.ac.cn/handle/2S8OKBNM/51655 |
| Collection | 中国科学院数学与系统科学研究院 |
| Corresponding Author | Li, Yue |
| Affiliation | 1.Nankai Univ, Coll Comp Sci, Tianjin 300350, Peoples R China 2.Key Lab Med Data Anal & Stat Res Tianjin KLMDASR, Tianjin 300350, Peoples R China 3.Chinese Acad Sci, Acad Math & Syst Sci, Natl Ctr Math & Interdisciplinary Sci, State Key Lab Sci & Engn Comp, Beijing 100190, Peoples R China |
| Recommended Citation GB/T 7714 | Wang, Wei,Wang, Chuang,Cui, Tao,et al. Study of Restrained Network Structures for Wasserstein Generative Adversarial Networks (WGANs) on Numeric Data Augmentation[J]. IEEE ACCESS,2020,8:89812-89821. |
| APA | Wang, Wei,Wang, Chuang,Cui, Tao,&Li, Yue.(2020).Study of Restrained Network Structures for Wasserstein Generative Adversarial Networks (WGANs) on Numeric Data Augmentation.IEEE ACCESS,8,89812-89821. |
| MLA | Wang, Wei,et al."Study of Restrained Network Structures for Wasserstein Generative Adversarial Networks (WGANs) on Numeric Data Augmentation".IEEE ACCESS 8(2020):89812-89821. |
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