이미지 자동 생성 인공지능의 원리와 현황

Abstract

This study identifies the principles of image generation artificial intelligence, examines its performance and limitations, and examines the innovative changes that have occurred in image production methods. Specifically, we reveal the structure of the Generative Adversarial Network (GAN), trace its technical improvement process in detail, and classify the types of subsequent models developed later. The tasks performed by GAN’s successor models are diverse. GAN is steadily evolving, completing amazing tasks that were impossible with existing generative models, such as high-resolution image generation, image substitution such as drawings and photos, and face synthesis. The principles of GAN are, first, data learning through adversarial competition between generator and discriminator, second, calculation of accurate probability distribution through Maximum Likelihood Estimation, and third, calculation of fine features of images through deep convolution structure. The most important characteristic of GAN as a generative model is its ability to create realistic images similar to photographs. Humans have been producing images using hands and tools, but with the invention of photography, humans began producing images using camera devices. Now, generative artificial intelligence is becoming another means of image production as technology continues to improve rapidly.