IMAGE TO IMAGE TRANSLATION USING GENERATIVE ADVERSARIAL NETWORKS

Abstract

The field of image-to-image translation faces several challenges, including issues related to model convergence speed and generated image quality. In response to these challenges, this project focuses on refining existing methodologies, specifically targeting the Pix2Pix and CycleGAN models. By substituting instance normalization with layer normalization in these architectures, we successfully overcome common hurdles, resulting in significantly improved convergence speeds and enhanced image quality. Through comprehensive experimentation, our adapted models demonstrate superior performance in generating high-quality images. A thorough comparison with InstructPix2Pix, a leading image translation model, further validates the effectiveness of our approach, positioning our modified architectures as competitive solutions in the realm of image-to-image translation. This project not only addresses key problems in the field but also contributes valuable insights for advancing the state of the art in image translation techniques.