Advanced Deep Learning with Keras

Chapter 7
Repeat for n training steps:
( )
D
1. Minimize L
forward−GAN
by training the forward-cycle discriminator using
real source and target data. A minibatch of real target data, y, is labeled
1.0. A minibatch of fake target data, y ' = G(x), is labelled 0.0.
( )
D
2. Minimize L
backward−GAN
by training the backward-cycle discriminator using
real source and target data. A minibatch of real source data, x, is labeled
1.0. A minibatch of fake source data, x ' = F(y), is labeled 0.0.
( )
G
3. Minimize L
GAN and L
cyc by training the forward-cycle and backward-cycle
generators in the adversarial networks. A minibatch of fake target data,
y ' = G(x), is labeled 1.0. A minibatch of fake source data, x ' = F(y), is labeled
1.0. The weights of discriminators are frozen.
Figure 7.1.4: During style transfer, the color composition may not be transferred successfully.
To address this issue, the identity loss is added to the total loss function.
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