Research and Application Progress of Generative Adversarial Networks
摘要
The Generative Adversarial Networks(GAN) based on the zero-sum game idea can obtain the distribution of data through unsupervised learning and generate more realistic data.Based on the basic concepts and theoretical framework of the generated confrontation network,the GAN models and the application results in specific fields are studied,and the data similarity measure,model framework and training method are summarized.The research and related research results of the improvement and expansion are analyzed,and the practical application fields such as image synthesis and style migration are discussed.The advantages and disadvantages of GAN are summarized,and the application prospects are prospected.Analysis results show that the GAN has strong learning ability and plasticity,great potential for improvement and wide application range.However,its development challenges are unstable training process and lack of objective evaluation criteria for generating data quality.