Systematic Analysis of Image Generation using GANs

TL;DR

This paper provides a comprehensive taxonomy and analysis of GAN-based image synthesis frameworks, highlighting their advantages, challenges, and potential future applications across various industries.

Contribution

It systematically categorizes GAN frameworks for image and text-to-image synthesis, critically analyzing their strengths and limitations compared to conventional methods.

Findings

GANs outperform traditional methods in image quality and diversity.

Frameworks enable high-resolution and art synthesis applications.

Challenges remain in implementation and application-specific customization.

Abstract

Generative Adversarial Networks have been crucial in the developments made in unsupervised learning in recent times. Exemplars of image synthesis from text or other images, these networks have shown remarkable improvements over conventional methods in terms of performance. Trained on the adversarial training philosophy, these networks aim to estimate the potential distribution from the real data and then use this as input to generate the synthetic data. Based on this fundamental principle, several frameworks can be generated that are paragon implementations in several real-life applications such as art synthesis, generation of high resolution outputs and synthesis of images from human drawn sketches, to name a few. While theoretically GANs present better results and prove to be an improvement over conventional methods in many factors, the implementation of these frameworks for dedicated applications remains a challenge. This study explores and presents a taxonomy of these frameworks and their use in various image to image synthesis and text to image synthesis applications. The basic GANs, as well as a variety of different niche frameworks, are critically analyzed. The advantages of GANs for image generation over conventional methods as well their disadvantages amongst other frameworks are presented. The future applications of GANs in industries such as healthcare, art and entertainment are also discussed.