  Generative Adversarial Networks (GANs) were introduced by Goodfellow in 2014,
and since then have become popular for constructing generative artificial
intelligence models. However, the drawbacks of such networks are numerous, like
their longer training times, their sensitivity to hyperparameter tuning,
several types of loss and optimization functions and other difficulties like
mode collapse. Current applications of GANs include generating photo-realistic
human faces, animals and objects. However, I wanted to explore the artistic
ability of GANs in more detail, by using existing models and learning from
them. This dissertation covers the basics of neural networks and works its way
up to the particular aspects of GANs, together with experimentation and
modification of existing available models, from least complex to most. The
intention is to see if state of the art GANs (specifically StyleGAN2) can
generate album art covers and if it is possible to tailor them by genre. This
was attempted by first familiarizing myself with 3 existing GANs architectures,
including the state of the art StyleGAN2. The StyleGAN2 code was used to train
a model with a dataset containing 80K album cover images, then used to style
images by picking curated images and mixing their styles.
