Research Highlights: Generative Adversarial Networks

Title of Paper: Generative Adversarial Networks

Author(s): Gilad Cohen and Raja Giryes

Abstract: 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.

Sign up for the free insideBIGDATA newsletter.

Join us on Twitter: @InsideBigData1 – https://twitter.com/InsideBigData1