Application and review of generative adversarial networks

Abstract

Generative adversarial networks (GANs) are one of the most popular innovations in machine learning. GANs provide a way to learn deep representations without extensively annotated training data. They achieve this by deriving backpropagation signals through a process involving a pair of networks. GANs are generative models since they are able to create data instances that resemble the training data. Besides, GANs provide a way to learn deep representations without annotated training data. They achieve this by deriving backpropagation signals through a looping process involving multiple networks. Those representations learned by GANs can be used in various fields such as Image Generation, Abnormal Detection, Video Repair, using GAN for Infrared to RGB, Image Inpainting, etc. This review paper provides a clear overview of GANs and their application into the Image Inpainting process. Furthermore, it points out both the advantages and disadvantages of GANs in the machine learning field.