Advanced Deep Learning with Keras

Disentangled
Representation GANs
As we've explored, GANs can generate meaningful outputs by learning the
data distribution. However, there was no control over the attributes of the outputs
generated. Some variations of GANs like Conditional GAN (CGAN) and Auxiliary
Classifier GAN (ACGAN), as discussed in the previous chapter are able to train
a generator that is conditioned to synthesize specific outputs. For example, both
CGAN and ACGAN can induce the generator to produce a specific MNIST digit.
This is achieved by using both a 100-dim noise code and the corresponding onehot
label as inputs. However, other than the one-hot label, we have no other ways
to control the properties of generated outputs.
For a review on CGAN and ACGAN, please see Chapter 4, Generative
Adversarial Networks (GANs), and Chapter 5, Improved GANs.
In this chapter, we will be covering the variations of GANs that enable us to
modify the generator outputs. In the context of the MNIST dataset, apart from
which number to produce, we may find that we want to control the writing style.
This could involve the tilt or the width of the desired digit. In other words, GANs
can also learn disentangled latent codes or representations that we can use to vary
the attributes of the generator outputs. A disentangled code or representation is
a tensor that can change a specific feature or attribute of the output data while
not affecting the other attributes.
[ 161 ]