generating realistic images using deep learning

Generative Models have shown huge improvements in recent years. Especially the fi eld of Generative Adversarial Networks (GANs) have proven useful for many different problems. In this report we will compare two kinds of generative models, which are GANs and Variational Autoencoders (VAEs). We apply those methods to different data sets, to point out their differences and to see their capabilities and limits as well: We fi nd that while VAEs are easier as well as faster to train, their results are in generell more blurry than the images generated by GANs. These on the other hand contain more details, which may realistic ones but often is just noise.

You can find the paper itself and the source code at GitHub