Imagine a scenario where we have pairs of images that are related to each other (for example, an image of 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 such a scenario, supervised learning does not help as much – as it learns only from history. A GAN comes in handy here because it will ensure that the generated image looks realistic enough and leaves room for experimentation (as we are interested in checking whether the generated image seems like one of the classes of interest or not).
In this section, we will learn about the architecture to generate the image of a shoe from a hand...
