Image-to-Image Translation is a technique in computer vision that transforms an input image into a different style or representation while preserving its essential content. This process often uses deep learning models, particularly Generative Adversarial Networks (GANs), to learn the mapping between the input and output images. Common applications of image-to-image translation include converting sketches into realistic images, changing the season in a landscape photo, or transforming day images into night scenes. This technology has significant implications in fields like art, design, and augmented reality, enabling creative and practical uses in various industries.