MUNIT: Unsupervised Image-to-Image Translation with Shared Latent Space

MUNIT stands for Multimodal Unsupervised Image-to-Image Translation. It is a deep learning model that can translate images from one domain to another without paired training data. It is a generative model that can learn to translate images between different visual domains, such as converting images from summer to winter, horses to zebras, or sketches to photographs. MUNIT uses a shared latent space to capture the style and content of images, allowing it to perform style transfer and image translation without the need for paired data. It achieves this by combining components of variational autoencoders (VAEs) and generative adversarial networks (GANs). MUNIT has been shown to produce high-quality and diverse translations between different visual domains.