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文章摘要
基于GAN等效模型的小样本库扩增研究
Amplification of small sample library based on GAN equivalent model
Received:August 14, 2018  Revised:August 14, 2018
DOI：
中文关键词: 小样本库  生成式对抗网络  等效模型  互相关系数  绝缘子
英文关键词: 
基金项目:
Author NameAffiliationE-mail
Gao Qiang North China Electric Power University gaoqiang0001@aliyun.com 
Jiang Zhonghao* North China Electric Power University jzh0633@163.com 
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中文摘要:
      在神经网络的训练中，训练样本库的数量对神经网络的性能有着重要的影响。利用深度神经网络技术对样本进行识别分类时，训练样本库的样本越多，识别得效果越好。因此对于小样本库来说，扩增训练样本库是提高神经网路性能的方法之一。生成式对抗网络(Generative Adversarial Nets, GAN)为扩增训练样本库提供了可行的解决方法。首先，分析了原始GAN的训练过程。根据GAN的工作过程，推导了生成器模型，得出了生成器模型符合维纳-霍普夫方程的结论，并对判别器符合最佳接收机模型做了进一步解释。并利用生成样本和训练样本之间的互相关系数证明了等效模型的正确性。在MNIST?CIFAR-10标准数据库上进行了实验，并依据实验结果，验证了等效模型的有效性。最后，将该等效模型应用到绝缘子样本库的扩增中，并取得了良好的效果。
英文摘要:
      
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