There are many new developments in the field of artificial intelligence, and one of the most exciting and transformative ideas are Generative Adversarial Networks (GANs). Here we explain in simple terms what they are.

1. AI Explained:
What Are
Networks (GANs)?
Generative Adversarial

2. © 2018 Bernard Marr, Bernard Marr & Co. All rights reserved
Title
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IntroductionIntroduction
The field of artificial intelligence (AI) is fast-moving, and new breakthroughs
are regularly made. One of the more recent terms rising to prominence is
Generative Adversarial Network (GAN) – but what does it mean?
Artificial Intelligence Explained: What Are
Generative Adversarial Networks (GANs)?

3. © 2018 Bernard Marr, Bernard Marr & Co. All rights reserved
The principle behind the GAN was first proposed in 2014, and at its most basic level, it
describes a system that pits two AI systems (neural networks) against each other to improve
the quality of their results.
To understand how they work, imagine a blind forger trying to create copies of paintings by
great masters. To start with, he has no idea what a painting should look like – but he happens
to have a friend who has a photographic memory of every masterpiece that's ever been
painted.
This friend – a detective – has to determine whether the paintings his friend is showing match
the features of those created by the real great masters, or are obvious forgeries.
This is the basic idea of how a GAN operates – only as they are AIs, both the forger and his
friend are able to act at super-speed, making and detecting thousands of forgeries per
second. Both of them then "learn" from the outcome to improve their future performance. As
the detective becomes better at detecting forgeries, the forger must become better at
creating them.
What Are GANs?

4. © 2018 Bernard Marr, Bernard Marr & Co. All rights reserved
GANs have been the cause of a lot of excitement within the field of AI development in recent
years, due to their ability to create “new” information following rules established by existing
information. An example might be writing instruction manuals. By training a GAN on
thousands of instruction manuals, it could one day be possible to create a system that could
look at any tool, device or software and then create instructions on how to use it.
So, let’s look into how this works in a bit more depth.
The “forger” network that creates fake data is termed the generative network, and its job is to
read and understand the properties of the training data. It then attempts to replicate it by
producing “candidate” datasets that follow the same rules.
The “detective” network tasked with determining whether the generative network is
outputting false (artificially generated) data or real (training) data is known as the
discriminative network. Because it competes against the generative network, the system as a
whole is described as "adversarial."
AI Development

5. © 2018 Bernard Marr, Bernard Marr & Co. All rights reserved
For a great working example of a GAN in action, look no further than the popular
demonstration This Person Does Not Exist. The network powering the website has learned to
produce ultra-realistic images of human faces that, while they follow all of the rules regarding
the way a human face should look, do not exist outside of the computer program.
While you might at first assume that the program builds images of faces by putting together
pieces from a database of eyes, ears, mouths, and hair, this isn't the case. The "input" data for
the generative network is simply a string of numbers – only the discriminative network sees
the training data. The generative network improves its output based entirely on the output of
the discriminative network.
As the only feedback the discriminative network gives is yes/no “guesses” at whether the
generative output matches the training data, it takes many, many attempts before it starts to
produce output that is acceptably close to the desired outputs – in this case, a realistic-
looking image of a non-existent person.
Case Study: This Person Does Not Exist

6. © 2018 Bernard Marr, Bernard Marr & Co. All rights reserved
(This example actually uses an updated model of the GAN known as proGAN which was
developed by Nvidia last year, and works by gradually increasing the resolution of the image
that the network generates, starting with a very low-resolution 4 pixel by 4 pixel image.)
The data used for training an adversarial network does not have to be labelled, as the
discriminative network can make judgments on the output of the generative network based
entirely on features of the training data itself. This means GANs have applications in
unsupervised learning as well as supervised (where the data is labeled) and reinforcement
learning.
Case Study: This Person Does Not Exist

7. © 2018 Bernard Marr, Bernard Marr & Co. All rights reserved
Another useful feature of GANs is that they can be used to efficiently create training datasets
for other AI applications. Most current AI techniques, in particular, Deep Learning, rely on
access to large amounts of data for training purposes.
GANs can generate datasets that follow all of the rules of "natural" datasets and so, in theory,
can be used for training of deep learning models. A great example of where this would be
useful might be medical images, that can be expensive and time-consuming to collect for real
– requiring both patient consent and medical expertise to label them.
GANs can be used for creating images, moving video images, text, and even music. While it's
clear that there is a lot of hype surrounding the concept at the moment, it is clearly one of the
most interesting new concepts to appear from the AI field in recent years, and we can expect
to see many exciting new applications based on it in the near future.
Training Datasets

8. © 2017 Bernard Marr , Bernard Marr & Co. All rights reserved
© 2018 Bernard Marr, Bernard Marr & Co. All rights reserved
Bernard Marr is an internationally best-selling author, popular keynote speaker, futurist, and a
strategic business & technology advisor to governments and companies. He helps
organisations improve their business performance, use data more intelligently, and
understand the implications of new technologies such as artificial intelligence, big data,
blockchains, and the Internet of Things.
LinkedIn has ranked Bernard as one of the world’s top 5 business influencers. He is a frequent
contributor to the World Economic Forum and writes a regular column for Forbes. Every day
Bernard actively engages his 1.5 million social media followers and shares content that
reaches millions of readers.
Visit The
Website
© 2017 Bernard Marr , Bernard Marr & Co. All rights reserved
© 2018 Bernard Marr, Bernard Marr & Co. All rights reserved
Bernard Marr is an internationally best-selling author, popular keynote speaker, futurist, and a
strategic business & technology advisor to governments and companies. He helps
organisations improve their business performance, use data more intelligently, and
understand the implications of new technologies such as artificial intelligence, big data,
blockchains, and the Internet of Things.
LinkedIn has ranked Bernard as one of the world’s top 5 business influencers. He is a frequent
contributor to the World Economic Forum and writes a regular column for Forbes. Every day
Bernard actively engages his 1.5 million social media followers and shares content that
reaches millions of readers.
Visit The
Website

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