Research Area:  Machine Learning

Abstract:

Generative Adversarial Networks (GANs) have received wide attention in the machine learning field for their potential to learn high-dimensional, complex real data distribution. Specifically, they do not rely on any assumptions about the distribution and can generate real-like samples from latent space in a simple manner. This powerful property allows GANs to be applied to various applications such as image synthesis, image attribute editing, image translation, domain adaptation, and other academic fields. In this article, we discuss the details of GANs for those readers who are familiar with, but do not comprehend GANs deeply or who wish to view GANs from various perspectives. In addition, we explain how GANs operates and the fundamental meaning of various objective functions that have been suggested recently. We then focus on how the GAN can be combined with an autoencoder framework. Finally, we enumerate the GAN variants that are applied to various tasks and other fields for those who are interested in exploiting GANs for their research.

Keywords:  

Author(s) Name:   Yongjun Hong , Uiwon Hwang , Jaeyoon Yoo , Sungroh Yoon

Publisher name:  ACM

DOI:  10.1145/3301282

Volume Information:  Volume 52,Issue 1,January 2020, Article No.: 10,pp 1–43

Paper Link:   https://dl.acm.org/doi/abs/10.1145/3301282