基于残差结构的对抗式网络图像生成方法

Image Generation Method for Adversarial Network Based on Residual Structure

生成对抗网络(GAN)是解决图像数据获取困难的有效方法,但GAN在训练时难以稳定,生成的图像质量较差。基于此,提出了一种基于残差结构的改进深度卷积GAN图像生成方法。采用残差结构加深网络并结合图像标签信息,以获取真实图像样本的深层次特征,在判别器模型中引入谱约束,提高网络的训练稳定性,从而实现图像数据的有效生成。实验表明,所提方法在生成图像的视觉效果和客观评价上具有更优异的表现。

Generative adversarial networks(GANs)effectively solve the difficulty of obtaining image data,but are disadvantaged by unstable training and poor quality of the generated images.To resolve these problems,this paper proposes an image generation method for an improved deep convolution GAN based on residual structures.The proposed method uses the residual structure to deepen the network and combines the image-label information to obtain the deep-level features of real image samples.It also introduces spectral constraints into the discriminator model,thereby improving the training stability of the network and the effective generation of the image data.The experimental results show that the proposed method has better performance in the visual effect and objective evaluation of the generated images.