基于子样本集构建的DCGANs训练方法

A Training Method of DCGANs Based on Subsample Set Construction

深度卷积生成式对抗网络(Deep convolutional generative adversarial networks,DCGANs)是一种改进的生成式对抗网络,尽管生成图像效果比传统GANs有较大提升,但在训练方法上依然存在改进的空间.本文提出了一种基于训练图像子样本集构建的DCGANs训练方法.推导给出了DCGANs的生成样本、子样本与总体样本的统计分布关系,结果表明子样本集分布越趋近于总体样本集,则生成样本集也越接近总体样本集.设计了基于样本一阶颜色矩和清晰度的特征空间的子样本集构建方法,通过改进的按概率抽样方法使得构建的子样本集之间近似独立同分布并且趋近于总体样本集分布.为验证本文方法效果,利用卡通人脸图像和Cifar10图像集,对比分析本文构建子样本集与随机选取样本的DCGANs训练方法以及其他训练策略实验结果.结果表明,在Batchsize约为2000的条件下,测试误差、KL距离、起始分数指标有所提高,从而得到更好的生成图像.

Deep convolutional generative adversarial networks(DCGANs)is an improved generative adversarial networks(GANs).There are some improvements in training method although the effect of generated images are better than traditional GANs.This work proposes a DCGANs training method based on training image subsample set construction.The statistical distribution relations of DCGANs generated samples,subsamples and all samples are derived.The results show that the distributions of subsample sets are closer to the whole sample set,and the generated sample set is closer to the whole sample set.And then,a subsample set construction method is designed based on sample first order color moment and sharpness feature space.These subsample sets are approximately independent identically distributed each other and similar to the whole sample set distribution by improved probability sampling method.To validate the effectiveness of this method,cartoon face image set and Cifar10 image set are used,the experimental results of DCGANs training method based on subsample set construction and random selection and other training strategies are compared and analyzed.The results show that under the condition that Batchsize is about 2000,the test error,KL divergence,inception score are improved,so that better images could be generated.