A Review of Image Style Transfer Using Generative Adversarial Networks Techniques
Main Article Content
Abstract
Generative Adversarial Networks (GANs) in style transfer have become widespread in digital art, entertainment, and creative sectors. The analysis of existing work on style transfer to create a snapshot of how GAN-based techniques convert images by taking the stylistic elements from one image and incorporating them into another has evolved. Additionally, it Will review some commonly used benchmark datasets for evaluating the performance of style transfer models. This underlying GAN architecture captures intricate features and representations of content and style images. This implies that by utilizing adversarial training coupled with specialized loss functions, our method can generate stylized images that are both high-fidelity while maintaining the content as desired. It is also demonstrated through a case study using a pre-trained GAN model which shows its potential for applications in real-life situations. Lastly, A model is proposed where an image style transfer framework improves the quality and consistency of stylized output. This framework aims to optimize the tradeoff between preserving content and applying styles to allow the creation of more advanced and reliable image style transfer systems under practical circumstances.