Advanced Deep Learning with Keras

Disentangled Representation GANs
In the first section of this chapter, we will be discussing InfoGAN: Interpretable
Representation Learning by Information Maximizing Generative Adversarial Nets [1],
an extension to GANs. InfoGAN learns the disentangled representations in an
unsupervised manner by maximizing the mutual information between the input
codes and the output observation. On the MNIST dataset, InfoGAN disentangles
the writing styles from digits dataset.
In the following part of the chapter, we'll also be discussing the Stacked Generative
Adversarial Networks or StackedGAN [2], another extension to GANs.
StackedGAN uses a pretrained encoder or classifier in order to aid in disentangling
the latent codes. StackedGAN can be viewed as a stack of models, with each being
made of an encoder and a GAN. Each GAN is trained in an adversarial manner by
using the input and output data of the corresponding encoder.
In summary, the goal of this chapter is to present:
• The concepts of disentangled representations
• The principles of both InfoGAN and StackedGAN
• Implementation of both InfoGAN and StackedGAN using Keras
Disentangled representations
The original GAN was able to generate meaningful outputs, but the downside
was that it couldn't be controlled. For example, if we trained a GAN to learn the
distribution of celebrity faces, the generator would produce new images of celebritylooking
people. However, there is no way to influence the generator on the specific
attributes of the face that we want. For example, we're unable to ask the generator for
a face of a female celebrity with long black hair, a fair complexion, brown eyes, and
whose smiling. The fundamental reason for this is because the 100-dim noise code
that we use entangles all of the salient attributes of the generator outputs. We can
recall that in Keras, the 100-dim code was generated by random sampling of uniform
noise distribution:
# generate 64 fake images from 64 x 100-dim uniform noise
noise = np.random.uniform(-1.0, 1.0, size=[64, 100])
fake_images = generator.predict(noise)
If we are able to modify the original GAN, such that we were able to separate the
code or representation into entangled and disentangled interpretable latent codes,
we would be able to tell the generator what to synthesize.
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