Example results on several image-to-image translation problems. In each case we use the same architecture and objective, simply training on different data. We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mappi
![Image-to-Image Translation with Conditional Adversarial Networks](https://cdn-ak-scissors.b.st-hatena.com/image/square/6b870810b4025ed245304f5176bb65911d3826e7/height=288;version=1;width=512/https%3A%2F%2Fphillipi.github.io%2Fpix2pix%2Fimages%2Fteaser_for_fb_v4.png)