Unpaired Image to Image Translation

Unpaired image-to-image translation aims to learn mappings between image domains without relying on paired examples, a significant challenge in computer vision. Current research focuses on improving the quality and consistency of translations using various architectures, including Generative Adversarial Networks (GANs) and diffusion models, often incorporating techniques like contrastive learning, optimal transport, and novel normalization layers to enhance performance. These advancements are crucial for diverse applications, such as medical image analysis, remote sensing, and autonomous driving, where paired data is scarce or expensive to acquire, enabling the creation of synthetic training data and cross-modal image harmonization.