Advanced Deep Learning with Keras

Disentangled Representation GANs
Figures 6.2.9 to 6.2.11 demonstrate that the StackedGAN has provided additional
control on the attributes of the generator outputs. The control and attributes are
(label, which digit), (z0, digit thickness), and (z1, digit tilt). From this example,
there are other possible experiments that we can control such as:
• Increasing the number of elements of the stack from the current 2
• Decreasing the dimension of codes z0 and z1, like in InfoGAN
Following figure shows the differences between the latent codes of InfoGAN and
StackedGAN. The basic idea of disentangling codes is to put a constraint on the
loss functions such that only specific attributes are affected by a code. Structure-wise,
InfoGAN are easier to implement when compared to StackedGAN. InfoGAN is also
faster to train:
Figure 6.2.12: Latent representations for different GANs
Conclusion
In this chapter, we've discussed how to disentangle the latent representations
of GANs. Earlier on in the chapter, we discussed how InfoGAN maximizes the
mutual information in order to force the generator to learn disentangled latent
vectors. In the MNIST dataset example, InfoGAN uses three representations and
a noise code as inputs. The noise represents the rest of the attributes in the form
of an entangled representation. StackedGAN approaches the problem in a different
way. It uses a stack of encoder-GANs to learn how to synthesize fake features
and images. The encoder is first trained to provide a dataset of features. Then,
the encoder-GANs are trained jointly to learn how to use the noise code to control
attributes of the generator output.
In the next chapter, we will embark on a new type of GAN that is able to generate
new data in another domain. For example, given an image of a horse, the GAN
can perform an automatic transformation to an image of a zebra. The interesting
feature of this type of GAN is that it can be trained without supervision.
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