Generative Adversarial Network (GAN) has gained eminence in a very short period as it can learn deep data distributions with the help of a competitive process among two networks. GANs can synthesize images/videos from latent noise with a minimized adversarial cost function. The cost function plays a deciding factor in GAN training and thus, it is often subjected to new modifications to yield better performance. To date, numerous new GAN models have been proposed owing to changes in cost function according to applications. The main objective of this research paper is to present a gist of major GAN publications and developments in image and video field. Several publications were selected after carrying out a thorough literature survey. Beginning from trends in GAN research publications, basics, literature survey, databases for performance evaluation parameters are presented under one umbrella.

生成对抗网络（GAN）在很短的时间内就获得了很高的知名度，因为它可以借助两个网络之间的竞争过程来学习深度数据分布。GAN可以利用最小化的对抗成本函数，从潜在噪声中合成图像/视频。成本函数在GAN训练中起决定性作用，因此，经常对其进行新的修改以产生更好的性能。迄今为止，由于成本函数根据应用的变化，已经提出了许多新的GAN模型。本研究论文的主要目的是介绍GAN在图像和视频领域的主要出版物和发展要旨。在进行了全面的文献调查后，选择了几本出版物。从GAN研究出版物，基础知识，文献调查的趋势开始，