Since you've come here to learn more about a specific neural network architecture, we're going to assume you have a baseline understanding of current machine and deep learning techniques that revolve around neural networks. Neural networks have exploded in popularity since the advent of the deep neural network-style architectures. By utilizing many hidden layers and large sums of data, modern deep learning techniques are able to exceed human-level performance in a dizzying number of applications. How is this possible? Neural networks are now able to learn baseline features and relationships in similar ways to our brains. Along those same lines, researchers have been exploring new styles of mixing neural networks to replicate the thought process that our brains take automatically.
The story is a classic: researcher goes drinking with a few friends and has an epiphany- what if you were able to pit two neural networks against each other to solve a problem? Ian Goodfellow, affectionately called the GANfather, helped popularize this adversarial architecture with his hallmark paper in 2014, called Generative Adversarial Networks. Researchers all over the world began developing variations on this technique: can you pit three or more networks against each other? What happens when you provide more than one loss function? These are actually the types of questions you should be able to answer by the end of this book, because we'll focus on implementing modern renditions of this architecture to solve these types of problems.
It's important to understand the difference and difficulties that surround generative and discriminative modeling. In recent years, discriminative modeling has seen some great successes. Typically requiring Markov decision processes in order for the generative modeling process to work, these techniques suffered from a lack of flexibility without heavy design tuning. That is, until the advent of the GANs architecture that we're discussing today. Goodfellow adequately summed up the issues surrounding discriminative and generative models in his paper in 2014:
Goodfellow and his coauthors presented a graphic on the challenges associated with generative modeling in the literature up until 2014
What are Goodfellow and his fellow authors getting at in this screenshot? Essentially, prior generative models were painful to train/build. GANs can have their challenges in terms of training and design, but represent a fundamental shift in flexibility in output given the ease of setup. In the Chapter 3, My First GAN in Under 100 Lines, we'll build a GAN network in under 100 lines of code.