Image to Image Translation

Image-to-image translation aims to transform images from one domain to another, preserving essential content while altering style or modality. Current research focuses on improving the quality and efficiency of this translation using various architectures, including Generative Adversarial Networks (GANs), diffusion models, and methods leveraging contrastive learning and optimal transport. These advancements are driving progress in diverse applications, such as medical image analysis, robotics, and the creation of synthetic datasets for training AI models, by enabling the generation of realistic and consistent translated images. Furthermore, efforts are underway to enhance the controllability and explainability of these translation processes.