In recent years, Generative Adversarial Networks (GAN) have rapidly developed and become one of the main research directions in the field of machine learning. GAN is based on the idea of zero-sum games, where its generator and discriminator learn in opposition to capture the data distribution of given samples, generating new sample data. A large amount of research has been conducted on the GAN model in various aspects such as image generation, anomaly detection and localization, text-to-image generation, and image super-resolution, with a systematic elaboration on the substantial progress made in these applications of GAN. This article summarizes the background and significance of GAN, the theoretical model and improved structures, as well as its main application areas. Through the analysis of GAN’s applications in various fields, it reviews the shortcomings of GAN and the directions for future development.
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