Opportunities and challenges in deep generative models

Abstract

A Generative Model is a powerful way of learning any kind of data distribution using unsupervised learning and it has achieved tremendous success in just few years. Though there are several approaches to design information systems for generating synthetic data, wich are referred to as Deep Generative Model (DGM). Since then, DGM has become a trending topic both in academic literature and industrial applications. It is also receiving increasing attention in machine learning competitions. This paper aims to provide an overview of the current progress towards DGM, as well as discussing its various applications and open problems for future research. Moreover, we discuss some research we conducted during last years that may extend the existing state of the art approaches in synthetic data generation or improving existing deep models