Generative Adversarial Networks (GANs) are part of the deep generative model family and able to generate synthetic samples based on the underlying distribution of real-world data. With expanding interest new discoveries and recent advances are hard to follow. Recent advancements to stabilize training, will help GANs to open up new domains using adjusted architectures and loss functions. Various findings show, that GANS can be used to generate not only images, but is also useful for text and audio creation. This paper, presents an overview of different GAN architectures, giving summaries of the underlying fundamentals of each presented GAN. Furthermore, this paper presents look into four application domains and lists additional domains. Additionally, this paper summaries datasets and metrics used to evaluate GANs and present recent scientific advancements. Keywords–generative adversarial networks; machine learning; deep learning.
