Advanced Deep Learning with Keras

Disentangled Representation GANs
The Encoder network is made of a stack of simple encoders, Encoder i
where i = 0 …
n - 1 corresponding to n features. Each encoder extracts certain facial features. For
example, Encoder 0
may be the encoder for hairstyle features, Features 1
. All the simple
encoders contribute to making the overall Encoder perform correct predictions.
The idea behind StackedGAN is that if we would like to build a GAN that generates
fake celebrity faces, we should simply invert the Encoder. StackedGAN are made
of a stack of simpler GANs, GAN i
where i = 0 … n - 1 corresponding to n features.
Each GAN i
learns to invert the process of its corresponding encoder, Encoder i
. For
example, GAN 0
generates fake celebrity faces from fake hairstyle features which is
the inverse of the Encoder 0
process.
Each GAN i
uses a latent code, z i
, that conditions its generator output. For example,
the latent code, z 0
, can alter the hairstyle from curly to wavy. The stack of GANs
can also act as one to synthesize fake celebrity faces, completing the inverse process
of the whole Encoder. The latent code of each GAN i
, z i
, can be used to alter specific
attributes of fake celebrity faces:
Figure 6.2.1: The basic idea of StackedGAN in the context of celebrity faces generation. Assuming that there is
a hypothetical deep encoder network that can perform classification on celebrity faces, a StackedGAN simply
inverts the process of the encoder.
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