Adversarial generative concept

How GANs work—an analogy 

To introduce how GANs work, let’s use an analogy:

A long, long time ago, there were two neighboring kingdoms on an island. One was called Netland, and the other was called Ganland. Both kingdoms produced fine wine, armor, and weapons. In Netland, the king demanded that the blacksmiths who specialized in making armor worked at the east corner of the castle, while those who made swords worked at the west side so that the lords and knights could choose the best equipment the kingdom had to offer. The king of Ganland, on the other hand, put all of the blacksmiths in the same corner and demanded that the armor makers and sword makers should test their work against each other every day. If a sword broke through the armor, the sword would sell at a good price, and the armor would be melted and reforged. If it didn’t, the sword would be remade, and men would strive to buy the armor. One day, the two kings were arguing over which kingdom made better wine until the quarrel escalated into war. Though outnumbered, the soldiers of Ganland wore the armor and swords that had been improved for years in the daily adversarial tests, and the Netland soldiers could not break their strong armor nor withstand their sharp swords. In the end, the defeated king of Netland, however reluctant he was, agreed that Ganland had better wine and blacksmiths.

Adversarial generative concept

Traditionally, generative problems are solved by statistics-based methods such as a Boltzmann machine, Markov chain, or variational encoder. As mathematically profound as they are, the generated samples are, as of yet, far from perfect. A classification model maps high-dimensional data to low-dimension, while a generative model often maps low-dimension data to high-dimension ones. People in both fields have been working hard to improve their models. Let’s look back to the little made-up opening story. Can we get the two different models to work against each other and improve themselves at the same time? If we take the output of a generative model as the input of the classification model, we can measure the performance of the generative model (the armor) with the classification model (the sword). At the same time, we can improve the classification model (the sword) by feeding generated samples (the armor) and real samples since we agree that more data is often better for training ML models.