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基于样本特征解码约束的GANs

陈泓佑 陈帆 和红杰 朱翌明

陈泓佑, 陈帆, 和红杰, 朱翌明. 基于样本特征解码约束的GANs. 自动化学报, 2020, 46(x): 1−13 doi: 10.16383/j.aas.c190496
引用本文: 陈泓佑, 陈帆, 和红杰, 朱翌明. 基于样本特征解码约束的GANs. 自动化学报, 2020, 46(x): 1−13 doi: 10.16383/j.aas.c190496
Chen Hong-You, Chen Fan, He Hong-Jie, Zhu Yi-Ming. A GANs based on sample feature decoding constraint. Acta Automatica Sinica, 2020, 46(x): 1−13 doi: 10.16383/j.aas.c190496
Citation: Chen Hong-You, Chen Fan, He Hong-Jie, Zhu Yi-Ming. A GANs based on sample feature decoding constraint. Acta Automatica Sinica, 2020, 46(x): 1−13 doi: 10.16383/j.aas.c190496

基于样本特征解码约束的GANs

doi: 10.16383/j.aas.c190496
基金项目: 国家自然科学基金(61872303, U1936113),四川省科技厅科技创新人才计划(2018RZ0143)资助
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

    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

    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

A GANs Based on Sample Feature Decoding Constraint

Funds: Supported by National Natural Science Foundation of China under Grants (61872303, U1936113), Technology Innovation Talent Program of Science & Technology Department of Sichuan Province (2018RZ0143)
  • 摘要: 生成式对抗网络(Generative Adversarial Networks, GANs)是一种有效模拟训练数据分布的生成模型, 其训练的常见问题之一是优化JS散度(Jensen-Shannon divergence)时可能产生梯度消失问题(Vanishing gradient problem). 针对该问题, 本文提出了一种解码约束条件下的GANs, 以尽量避免JS散度近似为常数而引发梯度消失现象, 从而提高生成图像的质量. 首先利用U-Net结构的自动编码机学习出与用于激发生成器的随机向量同维度的训练样本网络中间层特征. 然后在每次对抗训练前使用本文设计的解码约束条件训练解码器. 其中, 解码器与生成器结构相同, 权重共享. 为证明模型的可行性, 推导给出了引入解码约束条件有利于JS散度不为常数的结论以及解码损失函数的类型选择依据. 为验证模型的性能, 利用CELEBA和CIFAR10数据集, 对比分析了DCGANs, LSGANs, BEGANs, WGANs, WGANsGP 及SAGANs的生成效果. 通过实验对比IS, FID和清晰度等指标发现, 本文GANs能有效提高图像生成质量, 综合性能接近SAGANs.
  • 图  1  总体结构示意图

    Fig.  1  Overall structure sketch

    图  2  特征学习网络结构图

    Fig.  2  Structure diagram of feature learning network

    图  3  celeba数据集样本

    Fig.  3  Samples of celeba dataset

    图  4  cifar10数据集样本

    Fig.  4  Samples of cifar10 dataset

    图  5  U-Net自动编码示例

    Fig.  5  Samples of U-Net auto-encoder

    图  6  均匀特征实验样本, celeba

    Fig.  6  Uniform feature experimental samples, celeba

    图  7  L2解码不限制权重实验样本, celeba

    Fig.  7  L2 decoding with not restrict weight experimental samples, celeba

    图  8  本文方法实验样本, celeba

    Fig.  8  This paper method experimental samples, celeba

    图  9  均匀特征实验样本, cifar10

    Fig.  9  Uniform feature experimental samples, cifar10

    图  10  L2解码不限制权重实验样本, cifar10

    Fig.  10  L2 decoding with not restrict weight experimental samples, cifar10

    图  11  本文方法实验样本, cifar10

    Fig.  11  This paper method experimental samples, cifar10

    图  12  BEGANs实验样本, celeba

    Fig.  12  Experimental samples of BEGANs, celeba

    图  13  DCGANs实验样本, celeba

    Fig.  13  Experimental samples of DCGANs, celeba

    图  14  WGANsGP实验样本, celeba

    Fig.  14  Experimental samples of WGANsGP, celeba

    图  15  SAGANs1实验样本, celeba

    Fig.  15  Experimental samples of SAGANs1, celeba

    图  16  BEGANs实验样本, cifar10

    Fig.  16  Experimental samples of BEGANs, cifar10

    图  17  DCGANs实验样本, cifar10

    Fig.  17  Experimental samples of DCGANs, cifar10

    图  18  WGANsGP实验样本, cifar10

    Fig.  18  Experimental samples of WGANsGP, cifar10

    图  19  SAGANs1实验样本, cifar10

    Fig.  19  Experimental samples of SAGANs1, cifar10

    表  1  原图像与重构图像的PSNR和SSIM值统计

    Table  1  PSNR & SSIM between original and reconstructed images

    数据集 指标 均值 标准差 极小值 极大值
    celeba PSNR 40.588 5.558 22.990 61.158
    SSIM 0.9984 0.0023 0.9218 1.0000
    cifar10 PSNR 46.219 6.117 28.189 66.779
    SSIM 0.9993 0.0019 0.8180 1.0000
    下载: 导出CSV

    表  2  celeba中不同解码实验结果

    Table  2  Results of different decoding experiments in celeba

    对比项 IS ( $ \sigma \times 0.01 $ ) FID 清晰度均值 清晰度均值差值
    训练集 2.71±2.48 0.00 107.88 0.00
    正态特征 1.88±1.25 42.54 121.40 13.52
    均匀特征 1.82±1.48 43.04 123.02 15.14
    L1 1.99±1.53 32.95 120.16 12.28
    L2* 1.69±0.97 46.08 96.88 11.00
    L2(本文) 2.05±1.84 25.62 114.95 7.07
    黑体表示最优值, 下同
    下载: 导出CSV

    表  3  cifar10中不同解码实验结果

    Table  3  Results of different decoding experiments in cifar10

    对比项 IS ( $ \sigma \times 0.1 $ ) FID 清晰度均值 清晰度均值差值
    训练集 10.70±1.47 0.00 120.56 0.00
    正态特征 5.63±0.64 48.21 139.88 19.32
    均匀特征 5.51±0.79 46.57 137.13 16.57
    L1 5.63±0.79 42.70 138.04 17.48
    L2* 4.69±0.55 79.10 119.62 0.94
    L2(本文) 5.83±0.70 38.79 134.97 14.41
    下载: 导出CSV

    表  4  时间代价测试

    Table  4  Test of time cost

    数据集 模型 epoch数 总耗时/s 单位耗时/s
    celeba DCGANs 25 3,616.03 180.80
    本文 15 2,868.33 191.22
    cifar10 DCGANs 25 2,388.53 119.46
    本文 15 1,859.51 123.96
    下载: 导出CSV

    表  5  celeba中不同GANs对比

    Table  5  Comparsion of different GANs in celeba

    GANs模型 epoch数 优化项 参数量( $ \times 10^6 $ ) IS( $ \sigma \times 0.01 $ ) FID 清晰度均值 清晰度均值差值
    训练集 2.71±2.48 0.00 107.88 0.00
    BEGANs 35 W距离 4.47 1.74±1.29 46.24 77.58 30.3
    DCGANs 20 JS散度 9.45 1.87±1.58 50.11 124.82 16.94
    LSGANs 35 Pearson散度 9.45 2.02±1.63 39.11 122.19 14.31
    WGANs 35 W距离 9.45 2.03±1.75 40.31 117.15 9.27
    WGANsGP 35 W距离 9.45 1.98±1.82 37.01 121.16 13.28
    SAGANs1 30 W距离 10.98 2.06±1.79 21.94 109.94 2.06
    SAGANs2 30 JS散度 10.98 1.99±1.79 31.04 99.57 8.31
    本文 15 JS+ $ \lambda \cdot $ KL散度 9.45+0.84 2.05±1.84 25.62 114.95 7.07
    下载: 导出CSV

    表  6  cifar10中不同GANs对比

    Table  6  Comparsion of different GANs in cifar10

    GANs模型 epoch数 优化项 参数量( $ \times 10^6 $ ) IS( $ \sigma \times 0.1 $ ) FID 清晰度均值 清晰度均值差值
    训练集 10.70±1.47 0.00 120.56 0.00
    BEGANs 35 W距离 3.67 5.36±0.65 107.64 80.89 39.67
    DCGANs 20 JS散度 8.83 5.04±0.27 54.27 139.12 18.56
    LSGANs 35 Pearson散度 8.83 5.70±0.36 43.35 135.80 15.24
    WGANs 35 W距离 8.83 5.25±0.33 53.88 136.74 16.18
    WGANsGP 35 W距离 8.83 5.39±0.30 50.60 139.17 18.61
    SAGANs1 30 W距离 8.57 6.09±0.47 42.90 126.28 5.72
    SAGANs2 30 JS散度 8.57 5.37±0.46 53.49 133.54 12.98
    本文 15 JS+ $ \lambda \cdot $ KL散度 8.83+0.23 5.83±0.70 38.79 134.97 14.41
    下载: 导出CSV
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