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Generative
Adversarial Networks
In this chapter we will discuss Generative Adversarial Networks (GANs) and
its variants. GANs have been defined as the most interesting idea in the last 10 years
in ML (https://www.quora.com/What-are-some-recent-and-potentiallyupcoming-breakthroughs-in-deep-learning)
by Yann LeCun, one of the fathers
of deep learning. GANs are able to learn how to reproduce synthetic data that looks
real. For instance, computers can learn how to paint and create realistic images.
The idea was originally proposed by Ian Goodfellow (for more information refer to
NIPS 2016 Tutorial: Generative Adversarial Networks, by I. Goodfellow, 2016); he has
worked with the University of Montreal, Google Brain, and OpenAI, and is presently
working in Apple Inc as the Director of Machine Learning.
In this chapter we will cover different types of GANs and see some of their
implementation in TensorFlow 2.0. Broadly we will cover the following topics:
• What is a GAN?
• Deep convolutional GANs
• SRGAN
• CycleGAN
• Applications of GANs
What is a GAN?
The ability of GANs to learn high-dimensional, complex data distributions have
made them very popular with researchers in recent years. Between 2016, when
they were first proposed by Ian Goodfellow, up to 2019, we have more than 40,000
research papers related to GANs. This is in the space of just three years!
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