Generative Adversarial Network for Generating Different Types of Data

被引：0
作者：
Murota S. [1 ]
Iima H. [1 ]
机构：
[1] Kyoto Institute of Technology, Matsugasaki hashikamicho, Sakyo-ku, Kyoto
来源：
IEEJ Transactions on Electronics, Information and Systems | 2022年 / 142卷 / 07期
基金：
日本学术振兴会;
关键词：
deep generative model; deep learning; generative adversarial network; machine learning;
D O I：
10.1541/ieejeiss.142.781
中图分类号：
学科分类号：
摘要：
A generative adversarial network (GAN) is one of the popular deep generative models. It generates new data similar to the data of a dataset but is not intended to generate different data from them. In this paper, we propose a GAN that generates such different types of data, which a user desires to obtain. In the proposed method, some data of the dataset are iteratively exchanged for ones generated by the generator if the generated data are more helpful in generating the user's desirable ones. The performance of the proposed method is evaluated by comparing it with some other GANs. © 2022 The Institute of Electrical Engineers of Japan.
引用
收藏
页码：781 / 787
页数：6
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