Generative Adversarial Network for Synthetic Image Generation Method: Review, Analysis, and Perspective

Recently, generative adversarial networks (GANs) have been investigated since 2014, and many algorithmic solutions have been suggested for them. GAN have recently become a popular research topic. Even so, not many studies are deep enough to explain the relationship between the many variations of GAN and how they arise. We aim to provide a survey of different GAN techniques in this work, discussing them from the angles of theory, algorithms, and practical applications. We begin with a comprehensive introduction, architecture, and applications of the most popular GAN algorithms, then we draw parallels and draw distinctions between them. The second part of the study focuses on examining the theoretical issues associated with GANs. In this work, we try to determine the benefits and drawbacks of GANs, as well as the important obstacles that stand in the way of achieving successful implementation of GAN in a variety of application domains. Typical GAN applications are presented, including those in synthetic image processing and computer vision, natural language processing, music, speech, and audio, medicine, and data science. The final section of the research presents the study’s conclusion and some suggestions for further research. Further, highlighting the pros and cons of ongoing studies on the application of aversive learning can help guide future research efforts toward the most fruitful avenues.