Advanced Deep Learning with Keras

Chapter 7
return
dirs=dirs,
show=True)
# build the cyclegan for cifar10 colorization
models = build_cyclegan(shapes,
"gray-%s" % postfix,
"color-%s" % postfix,
kernel_size=kernel_size,
patchgan=patchgan)
# patch size is divided by 2^n since we downscaled the input
# in the discriminator by 2^n (ie. we use strides=2 n times)
patch = int(source_data.shape[1] / 2**4) if patchgan else 1
params = (batch_size, train_steps, patch, model_name)
test_params = (titles, dirs)
# train the cyclegan
train_cyclegan(models,
data,
params,
test_params,
other_utils.test_generator)
Generator outputs of CycleGAN
Figure 7.1.9 shows the colorization results of CycleGAN. The source images are
from the test dataset. For comparison, we show the ground truth and the colorization
results using a plain autoencoder described in Chapter 3, Autoencoders. Generally, all
colorized images are perceptually acceptable. Overall, it seems that each colorization
technique has both its own pros and cons. All colorization methods are not consistent
with the right color of the sky and vehicle.
For example, the sky in the background of the plane (3 rd row, 2 nd column) is white.
The autoencoder got it right, but the CycleGAN thinks it is light brown or blue.
For the 6 th row, 6 th column, the boat on the dark sea had an overcast sky but was
colorized with blue sky and blue sea by autoencoder and blue sea and white sky
by CycleGAN without PatchGAN. Both predictions make sense in the real world.
Meanwhile, the prediction of CycleGAN with PatchGAN is similar to the ground
truth. On 2 nd to the last row and 2 nd column, no method was able to predict the
red color of the car. On animals, both flavors of CycleGAN have closer colors to
the ground truth.
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