One of the hot topics in Deep Learning is Generative Adversarial Networks (GANs). Especially in the realm of Computer Vision, they opened the gates to fun and entertaining projects. Even though the real-world use cases for GANs are not as prominent and straightforward yet they have an aura of fascination as they have the ability to generate novel images.
As much fun as it is to toy around with GANs, it can also be very frustrating to work with them. Minor changes in the parameter settings can have a large impact on the quality of the generated images and finding adequate parameters can be tricky, as well as cost and time-intensive.
To succeed with GANs, here are a few things to consider when starting out with your project.