Effects of Dataset properties on the training of GANs

- Generative Adversarial Networks are a new family of generative models, frequently used for generating photorealistic images. The theory promises for the GAN to eventually reach an equilibrium where generator produces pictures indistinguishable for the training set. In practice, however, a range of problems frequently prevents the system from reaching this equilibrium, with training not progressing ahead due to instabilities or mode collapse. This paper describes a series of experiments trying to identify patterns in regard to the effect of the training set on the dynamics and eventual outcome of the training. Generating images is a task with many applications. As images are a compact and convenient format for communicating for humans, it is desirable for a computer to be able to generate such, as this would enable users to understand a wide range of messages and information faster and with ease. While there exist multiple software tools for generating images, for example photoshop, they are merely a way for a human to translate their idea into an image and take significant amount of effort and experience.