Generative adversarial networks (GANs) have been significantly investigated in the past few years due to its outstanding data generation capacity. The extensive use of the GANs techniques is dominant in the field of computer vision, for example, plausible image generation, image to image translation, facial attribute manipulation, improving image resolution, and image to text translation. In spite of the significant success achieved in these domains, applying GANs to various other problems still presents important challenges. Several reviews and surveys for GANs are available in the literature. However, none of them present short but focused review about the most significant aspects of GANs. In this paper, we address these aspects. We analyze the basic theory of GANs and the differences among various generative models. Then, we discuss the recent spectrum of applications covered by the GANs. We also provide an insight into the challenges and future directions.