Advanced Deep Learning with Keras

Chapter 6
Figure 6.1.7: The images generated by InfoGAN as the second continuous code is varied from -2.0 to 2.0 for
digits 0 to 9. The first continuous code is set to zero. The second continuous code controls the rotation angle
(tilt) of the writing style.
From these validation results, we can see that apart from the ability to generate
MNIST looking digits, InfoGAN expanded the ability of conditional GANs such
as CGAN and ACGAN. The network automatically learned two arbitrary codes
that can control the specific attributes of the generator output. It would be interesting
to see what additional attributes could be controlled if we increased the number
of continuous codes beyond 2.
StackedGAN
In the same spirit as InfoGAN, StackedGAN proposes a method for disentangling
latent representations for conditioning generator outputs. However, StackedGAN
uses a different approach to the problem. Instead of learning how to condition the
noise to produce the desired output, StackedGAN breaks down a GAN into a stack
of GANs. Each GAN is trained independently in the usual discriminator-adversarial
manner with its own latent code.
Figure 6.2.1 shows us how StackedGAN works in the context of the hypothetical
celebrity face generation. Assuming that the Encoder network is trained to classify
celebrity faces.
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