Image-to-Image Translation with Conditional Adversarial Networks

This paper introduces a general-purpose solution to image-to-image translation problems. It uses conditional adversarial networks to learn the mapping from an input image to an output image. The method is effective in synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, demonstrating its wide applicability across different tasks where a visual output is desired from a visual input.