Advanced Deep Learning with Keras

Cross-Domain GANs
Figure 7.1.14 shows CycleGAN reconstructing MNIST digits in the forward cycle.
The reconstructed MNIST digits are almost identical with the source MNIST digits.
Figure 7.1.15 shows the CycleGAN reconstructing SVHN digits in the backward
cycle. Many target images are reconstructed. Some digits are clearly the same
such as the 2 nd row last 2 columns (3 and 4). While some are the same but blurred
like 1 st row first 2 columns (5 and 2). Some digits are transformed to another digit
although the style remains like 2 nd row first two columns (from 33 and 6 to 1 and
an unrecognizable digit).
On a personal note, I encourage you to run the image translation by using the
pretrained models of CycleGAN with PatchGAN:
python3 cyclegan-7.1.1.py --mnist_svhn_g_source=cyclegan_mnist_svhn-g_
source.h5 --mnist_svhn_g_target=cyclegan_mnist_svhn-g_target.h5
Conclusion
In this chapter, we've discussed CycleGAN as an algorithm that can be used for
image translation. In CycleGAN, the source and target data are not necessarily
aligned. We demonstrated two examples, grayscale ↔ color, and MNIST ↔ SVHN.
Though there are many other possible image translations that CycleGAN can
perform.
In the next chapter, we'll embark on another type of generative model, Variational
AutoEncoders (VAEs). VAEs have a similar objective of learning how to generate
new images (data). They focus on learning the latent vector modeled as a Gaussian
distribution. We'll demonstrate other similarities in the problem being addressed
by GANs in the form of conditional VAEs and the disentangling of latent
representations in VAEs.
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