  Generative Adversarial Networks (GANs) were proposed in 2014 by Goodfellow et
al., and have since been extended into multiple computer vision applications.
This report provides a thorough survey of recent GAN research, outlining the
various architectures and applications, as well as methods for training GANs
and dealing with latent space. This is followed by a discussion of potential
areas for future GAN research, including: evaluating GANs, better understanding
GANs, and techniques for training GANs. The second part of this report outlines
the compilation of a dataset of images `in the wild' representing each of the 7
basic human emotions, and analyses experiments done when training a StarGAN on
this dataset combined with the FER2013 dataset.
