Advanced Deep Learning with Keras

Cross-Domain GANs
Similar to other GANs, the ultimate objective of CycleGAN is for the generator
G to learn how to synthesize fake target data, y ' , that can fool the discriminator,
D y
, in the forward cycle. Since the network is symmetric, CycleGAN also wants
the generator F to learn how to synthesize fake source data, x ' , that can fool the
discriminator, D x
, in the backward cycle. Inspired by the better perceptual quality
of Least Squares GAN (LSGAN) [5], as described in Chapter 5, Improved GANs,
CycleGAN also uses MSE for the discriminator and generator losses. Recall that the
difference of LSGAN from the original GAN is that the use of the MSE loss instead
of a binary cross-entropy loss. CycleGAN expresses the generator-discriminator loss
functions as:
( ) ( ) ( ( ))
( D)
2 2
L
forward− GAN
= E
y~ p
Dy y − 1 + E D
y x~
p x y
G x (Equation 7.1.7)
data
( )
( )
data
( ) 2
( )
D ( G
x
( x)
)
( G)
L
forward GAN
Ex~ p y
1 (Equation 7.1.8)
−
= −
data
( ) ( ( ) ) ( ) ( ( ))
( D)
2 2
L
backward− GAN
= Ex~ p
Dx x − 1 + E
x
y~
p
D
y x
F y (Equation 7.1.9)
data
data
( ) 2
( )
D ( F
y
( y)
)
( G)
L
backward GAN
E
y~ p x
1 (Equation 7.1.10)
−
= −
data
( D) ( D) ( D)
GAN
=
forward−GAN +
backward−GAN
L L L (Equation 7.1.11)
( D) ( D) ( D)
GAN
=
forward−GAN +
backward−GAN
L L L (Equation 7.1.12)
The total loss of CycleGAN is shown as:
L = λ L + λ L (Equation 7.1.13)
1 GAN 2
CycleGAN recommends the following weight values: λ
1
= 1.0 and λ
2
= 10.0 to give
more importance to the cyclic consistency check.
The training strategy is similar to the vanilla GAN. Algorithm 7.1.1 summarizes the
CycleGAN training procedure.
cyc
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