Abstract: Generative adversarial network (GAN) is currently a new and effective method for training generative model in image generation.As an extension of GAN,deep convolutional generative adversarial network (DCGAN) introduces convolutional neural networks into the generative model for unsupervised learning.However,the linear convolutional layer of DCGAN is a generalized linear model for the underlying data block.The abstraction level of DCGAN is low and the quality of the generated image is not high.In terms of model performance metrics,image quality is judged only by subjective visual perception.Aiming at the above problems,multi-layer perceptron deep convolutional generative adversarial network (MPDCGAN) was proposed,and the multi-layer perceptron convolutional layer was used to replace the generalized linear model to convolve the input data to capture the deeper features of the image.In order to evaluate the quality of the generated image,a quantitative evaluation method named Frechet Inception Distance (FID) was used.The experimental results on the four benchmark datasets show that the FID value of the image generated by MPDGAN is negatively correlated with the image quality,and the image quality is further improved with the decrease of the FID value.

Key words: Deep convolutional generative adversarial network, Frechet Inception Distance, Generative adversarial network, Multi-layer perceptron