  Generative Adversarial Networks (GANs) in supervised settings can generate
photo-realistic corresponding output from low-definition input (SRGAN). Using
the architecture presented in the SRGAN original paper [2], we explore how
selecting a dataset affects the outcome by using three different datasets to
see that SRGAN fundamentally learns objects, with their shape, color, and
texture, and redraws them in the output rather than merely attempting to
sharpen edges. This is further underscored with our demonstration that once the
network learns the images of the dataset, it can generate a photo-like image
with even a slight hint of what it might look like for the original from a very
blurry edged sketch. Given a set of inference images, the network trained with
the same dataset results in a better outcome over the one trained with
arbitrary set of images, and we report its significance numerically with
Frechet Inception Distance score [22].
