Research and Application Analysis of Generative Adversarial Network Technology

Generative Adversarial Networks (GANs) have revolutionized the field of generative modeling, achieving significant success in areas such as image generation, healthcare applications, and text-to-image translation. Their ability to produce highly realistic data has opened new possibilities across various industries. This paper comprehensively examines GANs, focusing on their fundamental structure and categorizing them into three main types. It highlights the pivotal role of their simple initial design in enabling subsequent advancements. However, real-world applications of GANs face notable challenges, including training instability, which often leads to mode collapse, ethical concerns such as deepfakes and privacy issues, and the high computational requirements that limit their practical use. This study delves into architectural improvements and task-specific adaptations, offering potential solutions to enhance training stability and optimize resource usage. This paper aims to provide a structured learning path for newcomers and researchers by addressing these critical issues while proposing strategies to advance GAN technology into new and emerging domains. Ultimately, this work contributes to a deeper understanding of GANs and their future potential.