Analysis of Image Generation by different Generator in GANs
Jiabei He, Yuzheng Nie, Ziwen Mao · Journal of Physics Conference Series · 2021
Abstract GAN is very useful in the field of image generation. Many related GANs have been proposed for generating images from the description. However, the research about analysis of image generation by different Generator in GANs is still insufficient. In this paper, different methods such as CNN and Resnet are used as the Generator in GANs for image generation. The subjective evaluation is used to analyze the performance of different generators of GAN. The result shows that the CNN-based generator performs better than the Resnet-based generator. And the increase of the number of parameters in the model will change the image quality. The analysis of different methods as the generator in GAN has a great referential significance in the field of image generation.