Chapter 1. Introduction to GANs

This chapter covers

  • An overview of Generative Adversarial Networks
  • What makes this class of machine learning algorithms special
  • Some of the exciting GAN applications that this book covers

The notion of whether machines can think is older than the computer itself. In 1950, the famed mathematician, logician, and computer scientist Alan Turing—perhaps best known for his role in decoding the Nazi wartime enciphering machine, Enigma—penned a paper that would immortalize his name for generations to come, “Computing Machinery and Intelligence.”

In the paper, Turing proposed a test he called the imitation game, better known today as the Turing test. In this hypothetical scenario, an unknowing observer talks with two counterparts behind a closed door: one, a fellow human; the other, a computer. Turing reasons that if the observer is unable to tell which is the person and which is the machine, the computer passed the test and must be deemed intelligent.

Anyone who has attempted to engage in a dialogue with an automated chatbot or a voice-powered intelligent assistant knows that computers have a long way to go to pass this deceptively simple test. However, in other tasks, computers have not only matched human performance but also surpassed it—even in areas that were until recently considered out of reach for even the smartest algorithms, such as superhumanly accurate face recognition or mastering the game of Go.[1]

1.1. What are Generative Adversarial Networks?

1.2. How do GANs work?

1.3. GANs in action

1.3.1. GAN training

1.3.2. Reaching equilibrium

1.4. Why study GANs?

Summary

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