I'm sure you've heard of a neural network dreaming? Maybe you've heard that AI is coming for you? Well, I'm here to tell you that there's no need to worry just yet. A Neural Network dreaming isn't too far away from the truth though. Generative Adversarial Networks (GANs), represent a shift in architecture design for deep neural networks. This new architecture pits two or more neural networks against each other in adversarial training to produce generative models. Throughout this book, we'll focus on covering the basic implementation of this architecture and then focus on modern representations of this new architecture in the form of recipes.
GANs are a hot topic of research today in the field of deep learning. Popularity has soared with this architecture style, with it's ability to produce generative models that are typically hard to learn. There are a number of advantages to using this architecture: it generalizes with limited data, conceives new scenes from small datasets, and makes simulated data look more realistic. These are important topics in deep learning because many techniques today require large amounts of data. Using this new architecture, it's possible to drastically reduce the amount of data needed to complete these tasks. In extreme examples, these types of architectures can use 10% of the data needed for other types of deep learning problems.
By the end of this chapter, you'll have learned about the following concepts:
- Do all GANs have the same architecture?
- Are there any new concepts within the GAN architecture?
- The basic construction of the GAN architecture in practice
Ready, set, go!
