Advanced Deep Learning with Keras

Cross-Domain GANs
We should note that PatchGAN is not introducing a new type of GAN in
CycleGAN. To improve the generated image quality, instead of having one output
to discriminate, we have four outputs to discriminate if we used a 2 × 2 PatchGAN.
There are no changes in the loss functions. Intuitively, this makes sense since the
whole image will look more real if every patch or section of the image looks real:
Figure 7.1.7: A comparison between GAN and PatchGAN discriminators
Following figure shows the discriminator network as implemented in Keras. The
illustration shows the discriminator determining how likely the input image or
a patch is a color CIFAR10 image. Since the output image is small at only 32 × 32
RGB, a single scalar representing that the image is real is sufficient. However, we
also evaluate the results when PatchGAN is used. Listing 7.1.3 shows the function
builder for the discriminator:
Figure 7.1.8: The target discriminator, D y
, implementation in Keras. The PatchGAN discriminator is shown on the right.
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