Advanced Deep Learning with Keras

Cross-Domain GANs
Figure 7.1.5: The CycleGAN model with identity loss as shown on the left side of the image
In neural style transfer problems, the color composition may not be successfully
transferred from source image to the fake target image. This problem is shown
in Figure 7.1.4. To address this problem, CycleGAN proposes to include the
forward and backward-cycle identity loss function:
⎡ ⎤ ⎡ ⎤
⎣ ⎦ ⎣ ⎦
L
identity
= Ex~ pdata
⎢ F x − x
1⎥ +
y~
p
G y − y (Equation 7.1.14)
data ⎢ 1⎥
The total loss of CycleGAN becomes:
( )
( ) E
x
( y) ( )
L = λ L + λ L + λ L (Equation 7.1.15)
1 GAN 2 cyc 3 identity
with λ
3
= 0.5 . The identity loss is also optimized during adversarial training.
Figure 7.1.5 shows CycleGAN with identity loss.
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