A UNIFIED DEEP LEARNING FRAMEWORK FOR CROSSDOMAIN IMAGE TRANSLATION USING STYLE EXEMPLAR

Abstract

Deep learning has significantly transformed the field of computer vision, particularly in image generation and translation tasks. Image translation refers to the process of converting an image from one domain into another while preserving its structural content and visual consistency. Traditional models such as Generative Adversarial Networks (GANs) and frameworks like CycleGAN and Pix2Pix have demonstrated promising results in various translation tasks; however, they often struggle with maintaining semantic consistency and transferring complex style characteristics between different domains. This research presents a unified deep learning framework for cross-domain image translation using style exemplars, where the visual style of a reference image is transferred to a target content image while preserving the structural layout of the original input. The proposed system integrates convolutional neural networks, attention mechanisms, and adversarial learning to effectively extract structural and appearance features from both images. By aligning content and style representations within a shared latent space, the framework enables meaningful semantic mapping between different visual domains. Multiple loss functions, including content loss, style loss, perceptual loss, and adversarial loss, are employed to guide the learning process and ensure high-quality translation results. Experimental evaluations using metrics such as FID, PSNR, and SSIM demonstrate that the proposed model generates visually realistic and semantically consistent images compared to existing approaches.