Image Translator: An Unsupervised Image-to-Image Translation Approach using GAN

Unpaired image-to-image translation uses unpaired training data to predict domain-to-domain mapping. Unpaired Image-to-Image is a versatile Generative Adversarial Network (GAN) model for image-to-image translation. Using the GAN architecture, you may train a generator model, which is often used to create graphics. To train the discriminator model, the generator is trained to fool the discriminator model into believing that the images are real. Generative adversarial networks (GAN) are a general-purpose solution for image-to-image translation challenges. These networks are concurrently learning a loss function that can be used to train the mapping they have learnt. This invention allows the use of the same fundamental strategy to solve problems that would otherwise need completely separate loss equations. We show that this approach may be used to synthesize photos from label maps, rebuild objects from edge maps, and colorize images.