Advanced Deep Learning with Keras

Chapter 7
kernel_size=kernel_size)
e3 = encoder_layer(e2,
128,
activation='leaky_relu',
kernel_size=kernel_size)
e4 = encoder_layer(e3,
256,
activation='leaky_relu',
kernel_size=kernel_size)
d1 = decoder_layer(e4,
e3,
128,
kernel_size=kernel_size)
d2 = decoder_layer(d1,
e2,
64,
kernel_size=kernel_size)
d3 = decoder_layer(d2,
e1,
32,
kernel_size=kernel_size)
outputs = Conv2DTranspose(channels,
kernel_size=kernel_size,
strides=1,
activation='sigmoid',
padding='same')(d3)
generator = Model(inputs, outputs, name=name)
return generator
The discriminator of CycleGAN is similar to vanilla GAN discriminator. The input
image is downsampled several times (in this example, three times). The final layer
is a Dense(1) layer which predicts the probability that the input is real. Each layer
is similar to the encoder layer of the generator except that no IN is used. However,
in large images, computing the image as real or fake with a single number turns
out to be parameter inefficient and results in poor image quality for the generator.
The solution is to use PatchGAN [6] which divides the image into a grid of patches
and use a grid of scalar values to predict the probability that the patches are real.
The comparison between the vanilla GAN discriminator and a 2 × 2 PatchGAN
discriminator is shown in Figure 7.1.7. In this example, the patches do not overlap
and meet at their boundaries. However, in general, patches may overlap.
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