Abstract

Generative adversarial networks have become increasingly popular since they were first introduced in 2014. Many variants of GANs have been developed over the years and employed in a range of applications from computer vision to audio generation and medical imaging. As its applications in computer vision have been widely explored by the artificial intelligence community, here, we focus on more specific applications of GANs, namely audio generation and medical image synthesis. In the age of big data, these two fields still struggle with the scarcity of labelled data, hence they benefit greatly from the capabilities of GANs.

Keywords: Audio Generation; Generative Adversarial Networks; Generative Models; Medical Image Synthesis