Conventional face image generation using generative adversarial networks (GAN) is limited by the quality of generated images since generator and discriminator use the same backpropagation network. In this paper, we discuss algorithms that can improve the quality of generated images, that is, high-quality face image generation. In order to achieve stability of network, we replace MLP with convolutional neural network (CNN) and remove pooling layers. We conduct comprehensive experiments on LFW, CelebA datasets and experimental results show the effectiveness of our proposed method.

中文翻译：

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基于生成对抗网络的高质量人脸图像生成

由于生成器和鉴别器使用相同的反向传播网络，因此使用生成对抗网络（GAN）的常规人脸图像生成受到生成图像质量的限制。在本文中，我们讨论了可以提高生成图像质量（即高质量人脸图像生成）的算法。为了实现网络的稳定性，我们用卷积神经网络（CNN）代替了MLP，并删除了池化层。我们对LFW，CelebA数据集进行了综合实验，实验结果表明了该方法的有效性。