  Generative Adversarial Networks (GANs) are very popular frameworks for
generating high-quality data, and are immensely used in both the academia and
industry in many domains. Arguably, their most substantial impact has been in
the area of computer vision, where they achieve state-of-the-art image
generation. This chapter gives an introduction to GANs, by discussing their
principle mechanism and presenting some of their inherent problems during
training and evaluation. We focus on these three issues: (1) mode collapse, (2)
vanishing gradients, and (3) generation of low-quality images. We then list
some architecture-variant and loss-variant GANs that remedy the above
challenges. Lastly, we present two utilization examples of GANs for real-world
applications: Data augmentation and face images generation.
