Leveraging the Pix2Pix GAN

Imagine a scenario where we have pairs of images that are related to each other (for example, an image of the edges of an object as input and an actual image of the object as output). The challenge given is that we want to generate an image given the input image of the edges of an object. In a traditional setting, this would have been a simple mapping of input to output and hence a supervised learning problem. However, imagine that you are working with a creative team that is trying to come up with a fresh look for products. In this scenario, supervised learning does not help much as it only learns from history. A GAN would come in handy here because it would ensure that the generated image would look realistic and would leave room for experimentation (as we are interested in checking whether the generated image is similar to the images that we want to generate). Specifically, Pix2Pix GAN comes in handy in scenarios in which it is trained to generate an...