Advanced Deep Learning with Keras

Improved GANs
Essentially, in CGAN we feed the network with side information (label). In ACGAN,
we try to reconstruct the side information using an auxiliary class decoder network.
ACGAN argued that forcing the network to do additional tasks is known to improve
the performance of the original task. In this case, the additional task is image
classification. The original task is the generation of fake images.
Network Loss Functions Number
CGAN ( D)
L =−E
x~ p
log D( x | y) −Ez
log 1 −D D( z | y)
L
( G)
data
=−E z
log D( G( z | y)
)
( ( ))
ACGAN ( D)
L =−E log D( x) −E log 1 −D( G ( z | y)
) −E log P c | x −E
log P ( c | G z | y )
( ) ( ) ( )
x~ pdata
z x~
pdata
z
( G)
L =−E
log D( G( z | y)
) −E
log P ( c | G( z | y)
)
z
z
4.3.1
4.3.2
5.3.1
5.3.2
Table 5.3.1: A comparison between the loss functions of CGAN and ACGAN
Preceding table shows the ACGAN loss functions as compared to CGAN.
The ACGAN loss functions are the same as CGAN except for the additional
classifier loss functions. Apart from the original task of identifying real from fake
images ( −E
x~ p
log D( x | y) −E
log( 1 ( ( | )))
data
z
−D G z y ), Equation 5.3.1 of the discriminator
has the additional task of correctly classifying real and fake images
( −E
x~ p
log P ( c | x) −E
log ( | ( | ))
data
z
P c G z y ). Equation 5.3.2 of the generator
means that apart from trying to fool the discriminator with fake images
( −E
z
log D( G ( z | y)
)), it is asking the discriminator to correctly classify those
fake images ( −E
z
log P ( c | G ( z | y)
) ).
Starting with the CGAN code, only the discriminator and the training function are
modified to implement ACGAN. The discriminator and generator builder functions
are also provided by gan.py. To see the changes made on the discriminator,
following listing shows the builder function where the auxiliary decoder network
that performs image classification and the dual outputs are highlighted.
Listing 5.3.1, gan.py shows how the discriminator model builder is the same as
in DCGAN predicting if an image is real, the first output. An auxiliary decoder
network is added to perform the image classification and produce the second output:
def discriminator(inputs,
activation='sigmoid',
num_labels=None,
num_codes=None):
"""Build a Discriminator Model
[ 148 ]