Analysis of Learning Mechanism of Generative Adversarial Network

Abstract

The generative adversarial network learns different kinds of real images and generates corresponding fake images. The image quality generated based on the generative adversarial network show the strength of the learning ability of the model for different kinds of images. Based on the apparent differences in features of different types of images, this paper proposes to judge the strength of features of different kinds of images in the generative adversarial network learning process based on generative adversarial networks and convolutional neural networks. The experiment uses three different kinds of data sets, including cartoon, face and food, and carries out three groups of experiments. The experimental results show that the simpler the image is, the stronger the learning ability is.