Image Style Transfer with GANs

Style transfer, the consequence of the application of the style of one image to the content of another, has become an emerging form of applying deep learning in the arts field. Employing Generative Adversarial Networks (GANs), this study discusses newer techniques for improving style-content fusion instead of basic convolutions such as Neural Style Transfer (NST). This paper uses GANs to generate stylized images for unpaired image-to-image translation and non-destructive content distortions. This paper begins with an introduction to the fundamental concepts. It compares and outlines current sophisticated GAN techniques such as Cycle GAN and StyleGAN and their contributions to generating enhanced aesthetic and photorealistic images. In this regard, the present research seeks to answer the following questions: How do the chosen weak points impact the general performance of style transfer methods? Given the weak spots, how can it be reevaluated and improved to enhance the efficiency of style transfer frameworks for future art, design, and multimedia applications.