Advanced Deep Learning with Keras

Generative Adversarial Networks (GANs)
A great example of the advanced features of GANs [4] can be seen with this
YouTube video (https://youtu.be/G06dEcZ-QTg). The video, which shows how
GANs can be utilized to produce realistic faces just shows how powerful they can
be. This topic is much more advanced than anything we've looked at before in this
book. For example, the above video is something that can't be accomplished easily
by autoencoders, which we covered in Chapter 3, Autoencoders.
GANs are able to learn how to model the input distribution by training two
competing (and cooperating) networks referred to as generator and discriminator
(sometimes known as critic). The role of the generator is to keep on figuring out
how to generate fake data or signals (this includes, audio and images) that can
fool the discriminator. Meanwhile, the discriminator is trained to distinguish
between fake and real signals. As the training progresses, the discriminator will
no longer be able to see the difference between the synthetically generated data
and the real ones. From there, the discriminator can be discarded, and the generator
can now be used to create new realistic signals that have never been observed before.
The underlying concept of GANs is straightforward. However, one thing
we'll find is that the most challenging aspect is how do we achieve stable training
of the generator-discriminator network? There must be a healthy competition
between the generator and discriminator in order for both networks to be able
to learn simultaneously. Since the loss function is computed from the output
of the discriminator, its parameters update is fast. When the discriminator
converges faster, the generator no longer receives sufficient gradient updates for
its parameters and fails to converge. Other than being hard to train, GANs can also
suffer from either a partial or total modal collapse, a situation wherein the generator
is producing almost similar outputs for different latent encodings.
Principles of GANs
As shown in Figure 4.1.1 a GAN is analogous to a counterfeiter (generator) - police
(discriminator) scenario. At the academy, the police are taught how to determine if
a dollar bill is either genuine or fake. Samples of real dollar bills from the bank and
fake money from the counterfeiter are used to train the police. However, from time
to time, the counterfeiter will attempt to pretend that he printed real dollar bills.
Initially, the police will not be fooled and will tell the counterfeiter why the money
is fake. Taking into consideration this feedback, the counterfeiter hones his skills
again and attempts to produce new fake dollar bills. As expected the police will
be able to both spot the money as fake and justify why the dollar bills are fake.
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