The performance analysis of generative adversarial networks

Generative Adversarial Networks have been widely used in recent years. Some extensions, such as Deep Convolutional Generative Adversarial Networks have also appeared. Currently, there is no research on the results generated by different pairings of existing generators and discriminators. Therefore, the research topic of this paper is to find the pairing combination of generator and discriminator to generate the best images. The research methods of this paper are as follows: Based on the Celeb-A Faces face dataset, the generated results were compared and analyzed by changing the generator and discriminator and considering the influence of the number of training epochs on the results to find the optimal combination. This paper has chosen two convolutional neural network generators, and discriminators, each of them has 3 and 5 layers of convolutional layers and one multilayer perceptron generator and discriminator. It is found that the generator and discriminator with higher convolutional layers generate clearer images. Multilayer perceptron generators and discriminators produce poor quality results. The larger the number of training epochs is, the better the generation effect is within a specific range. Therefore, selecting generators and discriminators with high convolutional layers is suggested for training.