Image Generation Approach with Generative Adversarial Networks

Abstract

Social networking sites and smartphones are frequently utilised, photo editing software is widely available, and interest in more sophisticated photo editing methods is expanding. Converting images into different styles is one of these processing methods. In recent years, numerous studies have looked into employing machine learning to generate images automatically as a solution to this issue. Modern frameworks that use Generative Adversarial Networks (GANs) have shown amazing results for a variety of applications in many domains, particularly those connected to picture production, because they can produce extremely realistic and sharp images as well as train on enormous data sets. These techniques have also yielded extremely effective outcomes. Many researches have been done utilizing this technique to change photos into anime-style images. Yet, the majority of this research is just concerned with how the facial part and background change in style. The objective of this task is to create an image of a person from a photograph that resembles an animated character.