| 陈 亮,吴 攀,刘韵婷,刘晓阳,杨佳明,姜 余.生成对抗网络 GAN 的发展与最新应用[J].电子测量与仪器学报,2020,34(6):70-78 |
| 生成对抗网络 GAN 的发展与最新应用 |
| Development and application of the latest generation against the network of GAN |
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| DOI： |
| 中文关键词: 机器学习 对抗学习 生成对抗网络 理论模型 |
| 英文关键词:machine learning confrontational learning generating confrontation network(GAN) theoretical model |
| 基金项目:国家重点研发计划(2017YFC0821001)、国家重点研发计划(2017YFC0821004-5)、辽宁省教育厅基本科研项目(LG201707)、辽宁省自然科学基金(20170540788)资助项目 |
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| 中文摘要: |
| 近年来,生成式对抗网络(generative adversarial nets, GAN)迅速发展,已经成为当前机器学习领域的主要研究方向之
一。 GAN 来源于零和博弈的思想,其生成器和鉴别器对抗学习,获取给定样本的数据分布,生成新的样本数据。 对 GAN 模型
在图片生成、异常样本检测和定位、文字生成图片以及图片超分辨率等多方面进行了大量的调查研究,并在这些 GAN 的应用所
取得的实质性进展进行了系统的阐述。 对 GAN 的提出背景与研究意义、理论模型与改进结构,以及其主要应用领域进行了总
结。 通过对 GAN 在各方面的应用分析,对 GAN 的不足以及未来发展方向进行综述。 |
| 英文摘要: |
| In recent years, generative adversarial nets ( GANs) have developed rapidly and have become one of the main research
directions in the current machine learning field. GAN is derived from the idea of zero-sum game. Its generator and discriminator are
opposed to learning. The purpose is to obtain the data distribution of a given sample and generate new sample data. A large number of
investigations have been made on GAN models in image generation, abnormal sample detection and location, text generation pictures and
picture super-resolution. The substantial progress made in the application of these GANs has been systematically explained. The
background and research significance, theoretical model and improved structure of GAN, and its main application fields are summarized.
The shortcomings of GAN and its future development direction were summarized. |
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