This neural network can swap sheep for giraffes, jeans for skirts

It might sound far-fetched, but those are just a couple of the feats a machine learning algorithm designed by researchers at the Korea Advanced Institute of Science and Technology and the Pohang University of Science and Technology can accomplish after ingesting large datasets of images. It's described in a new paper ("InstaGAN: Instance-Aware Image-to-Image Translation") published on the preprint server Arxiv.org this week. Image-to-image translation systems -- that is, systems that learn the mapping from input image to output image -- aren't anything new, to be clear. Only earlier this month, Google AI researchers developed a model that can realistically insert an object in a photo by predicting its scale, occlusions, pose, shape, and more. But as the creators of InstaGAN wrote in the paper, even state-of-the-art methods aren't perfect.