Introduction to Adversarial Autoencoders

Generative Adversarial Networks (GAN) shook up the deep learning world. When they first appeared in 2014, they proposed a new and fresh approach to modeling and gave a possibility for new neural network architectures to emerge. Since standard GAN architecture is composed from two neural networks, we can play around and use different approaches for those networks and thus create new and shiny architectures. The idea is to make an appropriate model for your problem and generate data which can be used in a real-world business scenario. So far, we had a chance to see how to implement the standard GAN and Deep Convolutional GAN (combining CNN concepts with GAN concepts), but the zoo of GAN architectures grows on a daily basis.