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基于子样本集构建的DCGANs训练方法

陈泓佑 和红杰 陈帆 朱翌明

陈泓佑, 和红杰, 陈帆, 朱翌明. 基于子样本集构建的DCGANs训练方法.自动化学报, 2021, 47(4): 913-923 doi: 10.16383/j.aas.c180677
引用本文: 陈泓佑, 和红杰, 陈帆, 朱翌明. 基于子样本集构建的DCGANs训练方法.自动化学报, 2021, 47(4): 913-923 doi: 10.16383/j.aas.c180677
Chen Hong-You, He Hong-Jie, Chen Fan, Zhu Yi-Ming. A training method of DCGANs based on subsample set construction. Acta Automatica Sinica, 2021, 47(4): 913-923 doi: 10.16383/j.aas.c180677
Citation: Chen Hong-You, He Hong-Jie, Chen Fan, Zhu Yi-Ming. A training method of DCGANs based on subsample set construction. Acta Automatica Sinica, 2021, 47(4): 913-923 doi: 10.16383/j.aas.c180677

基于子样本集构建的DCGANs训练方法

doi: 10.16383/j.aas.c180677
基金项目: 

国家自然科学基金 61872303

四川省科技厅科技创新人才计划 2018RZ0143

详细信息
    作者简介:

    陈泓佑  西南交通大学信息科学与技术学院博士研究生. 主要研究方向为机器学习, 图像处理. E-mail: chy2019@foxmail.com

    陈帆  西南交通大学信息科学与技术学院副教授. 主要研究方向为多媒体安全, 计算机应用. E-mail: fchen@home.swjtu.edu.cn

    朱翌明  西南交通大学信息科学与技术学院硕士研究生. 主要研究方向为深度学习, 图像处理. E-mail: swjtu163zym@163.com

    通讯作者:

    和红杰  西南交通大学信息科学与技术学院教授. 主要研究方向为图像取证, 图像处理. 本文通信作者. E-mail: hjhe@home.swjtu.edu.cn

A Training Method of DCGANs Based on Subsample Set Construction

Funds: 

National Natural Science Foundation of China 61872303

Technology Innovation Talent Program of Science & Technology Department of Sichuan Province 2018RZ0143

More Information
    Author Bio:

    CHEN Hong-You  Ph. D. candidate at the School of Information Science and Technology, Southwest Jiaotong University. His research interest covers machine learning, and image processing

    CHEN Fan  Associate professor at the School of Information Science and Technology, Southwest Jiaotong University. His research interest covers multimedia security and computer applications

    ZHU Yi-Ming  Master student at the School of Information Science and Technology, Southwest Jiaotong University. His research interest covers deep learning and image processing

    Corresponding author: HE Hong-Jie  Professor at the School of Information Science and Technology, Southwest Jiaotong University. Her research interest covers image forensics, and image processing. Corresponding author
  • 摘要: 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2 000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.
    Recommended by Associate Editor ZHANG Jun-Ping
    1)  本文责任编委 张军平
  • 图  1  DCGANs训练示意图

    Fig.  1  Schematic diagram of DCGANs training

    图  2  G网络学习的中间效果

    Fig.  2  Intermediate effects of G net learning

    图  3  卡通人脸训练集样本

    Fig.  3  Training set samples of cartoon face

    图  4  Cifar10训练集样本

    Fig.  4  Training set samples of Cifar10

    图  5  低频样本和普通样本

    Fig.  5  Low frequency and common samples

    图  6  生成样本(随机, Batchsize = 2 000, 卡通人脸)

    Fig.  6  Generated samples (random, 2 000, cartoon face)

    图  7  生成样本(构建, Batchsize = 2 000, 卡通人脸)

    Fig.  7  Generated samples (constructing, 2 000, cartoon face)

    图  8  生成样本(随机, Batchsize 2 048, Cifar10)

    Fig.  8  Generated samples (random, 2 048, Cifar10)

    图  9  生成样本(构建, Batchsize = 2 048, Cifar10)

    Fig.  9  Generated samples (constructing, 2 048, Cifar10)

    图  10  生成样本(128 (文献[7]), 卡通人脸)

    Fig.  10  Generated samples (128 (paper [7]), cartoon face)

    图  11  生成样本(128 (正则化), 卡通人脸)

    Fig.  11  Generated samples (128 (regularizer), cartoon face)

    图  12  生成样本(128 (文献[7]), Cifar10)

    Fig.  12  Generated samples (128 (paper [7]), Cifar10)

    图  13  生成样本(128 (正则化), Cifar10)

    Fig.  13  Generated samples (128 (regularizer), Cifar10)

    表  1  不同Batchsize下总体覆盖率

    Table  1  Total coverage rate of different Batchsize

    数据集 Batchsize 构建采样(%) 随机采样(%) 差距值(%)
    卡通人脸 512 80.68 99.96 19.28
    1 024 89.20 99.96 10.76
    2 000 93.20 97.59 4.39
    Cifar10 512 78.57 99.33 20.76
    1 024 87.54 98.30 10.76
    2 048 92.52 98.30 5.78
    下载: 导出CSV

    表  2  不同Batchsize下$ KL(f_{X_i}(x)||f_X(x)) $数据

    Table  2  $ KL(f_{X_i}(x)||f_X(x)) $ data under difierent Batchsize

    数据集 Batchsize 均值 标准差 最小值 中值 最大值
    卡通人脸 128 1.3375 0.0805 1.1509 1.3379 1.6156
    1 024 0.3109 0.0147 0.2849 0.3110 0.3504
    1 024* 0.2366 0.0084 0.2154 0.2365 0.2579
    2 000 0.1785 0.0089 0.1652 0.1778 0.1931
    2 000* 0.1144 0.0042 0.1049 0.1150 0.1216
    Cifar10 128 1.4125 0.0772 1.1881 1.4155 1.6037
    1 024 0.3499 0.0155 0.3215 0.3475 0.3886
    1 024* 0.2692 0.0063 0.2552 0.2687 0.2836
    2 048 0.1994 0.0085 0.1830 0.2004 0.2148
    2 048* 0.1372 0.0040 0.1281 0.1372 0.1462
    带"*"项是构建子样本集相关数据, 下同
    下载: 导出CSV

    表  3  卡通人脸数据集实验结果对比

    Table  3  Experimental results comparison of cartoon face dataset

    Batchsize epoch 测试误差($ \times10^{-3} $) KL IS ($ \sigma\times10^{-2} $)
    1 024 135 8.03 $ \pm $ 2.12 0.1710 3.97 $ \pm $ 2.62
    1 024* 135 8.23 $ \pm $ 2.10 0.1844 3.82 $ \pm $ 2.02
    2 000 200 7.68 $ \pm $ 2.21 0.1077 3.95 $ \pm $ 2.32
    2 000* 200 7.18 $ \pm $ 2.13 0.0581 4.21 $ \pm $ 2.53
    下载: 导出CSV

    表  4  Cifar10数据集实验结果对比

    Table  4  Experimental results comparison of Cifar10 dataset

    Batchsize epoch 测试误差($ \times10^{-2} $) KL IS ($ \sigma\times10^{-2} $)
    1 024 100 1.43 $ \pm $ 0.38 0.2146 5.44 $ \pm $ 6.40
    1 024* 100 1.48 $ \pm $ 0.35 0.2233 5.36 $ \pm $ 6.01
    2 048 200 1.40 $ \pm $ 0.39 0.2095 5.51 $ \pm $ 5.83
    2 048* 200 1.35 $ \pm $ 0.37 0.1890 5.62 $ \pm $ 5.77
    下载: 导出CSV

    表  5  卡通人脸数据集不同策略对比

    Table  5  Different strategies comparison of cartoon face dataset

    Batchsize epoch 测试误差($ \times10^{-3} $) KL IS ($ \sigma\times10^{-2} $)
    1 024* 135 8.23 $ \pm $ 2.10 0.1844 3.82 $ \pm $ 2.02
    2 000* 200 7.18 $ \pm $ 2.13 0.0581 4.21 $ \pm $ 2.53
    128 (a) 25 8.32 $ \pm $ 2.07 0.1954 3.62 $ \pm $ 2.59
    128 (b) 25 8.15 $ \pm $ 2.15 0.1321 3.92 $ \pm $ 4.59
    128 (c) 25 8.07 $ \pm $ 2.10 0.1745 3.89 $ \pm $ 4.45
    128 (d) 25 8.23 $ \pm $ 2.26 0.1250 4.02 $ \pm $ 3.97
    下载: 导出CSV

    表  6  Cifar10数据集不同策略对比

    Table  6  Different strategies comparison of Cifar10 dataset

    Batchsize epoch 测试误差($ \times10^{-2} $) KL IS ($ \sigma\times10^{-2} $)
    1 024* 100 1.48 $ \pm $ 0.35 0.2233 5.36 $ \pm $ 6.01
    2 048* 200 1.35 $ \pm $ 0.37 0.1890 5.62 $ \pm $ 5.77
    128 (a) 25 1.81 $ \pm $ 0.41 0.2813 4.44 $ \pm $ 3.66
    128 (b) 25 1.64 $ \pm $ 0.40 0.2205 4.61 $ \pm $ 3.80
    128 (c) 25 1.70 $ \pm $ 0.41 0.2494 4.62 $ \pm $ 4.80
    128 (d) 25 1.63 $ \pm $ 0.42 0.2462 4.94 $ \pm $ 5.79
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-10-18
  • 录用日期:  2019-04-15
  • 刊出日期:  2021-04-23

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