Image Representation Learning with Generative Adversarial Networks - Falco Winkler (2018)
Generative machine learning models make it possible to derive new data from a dataset. There are many applications in image processing, such as super-resolution and image completion. But they also find application in engineering and in other machine learning algorithms. A generative adversarial network (GAN) is trained by pitting a neural net that generates data out of a noise input, against an adversary, that tries to discriminate between fake and real data. This thesis is about the theory of GAN’s and their application on the datasets MNIST and CIFAR10 in an unsupervised and semi-supervised fashion. A state of the art and fully automated implementation is used to achieve high quality image generation.