Advanced Deep Learning with Keras

Cross-Domain GANs
In this chapter, we will explore the following:
• The principles of CycleGAN, including its implementation in Keras
• Example applications of CycleGAN, including the colorization of grayscale
images using the CIFAR10 dataset and style transfer as applied on MNIST
digits and Street View House Numbers (SVHN) [1] datasets
Principles of CycleGAN
Figure 7.1.1: Example of aligned image pair: left, original image and right, transformed image
using a Canny edge detector. Original photos were taken by the author.
Translating an image from one domain to another is a common task in computer
vision, computer graphics, and image processing. The preceding figure shows
edge detection which is a common image translation task. In this example, we can
consider the real photo (left) as an image in the source domain and the edge detected
photo (right) as a sample in the target domain. There are many other cross-domain
translation procedures that have practical applications such as:
• Satellite image to map
• Face image to emoji, caricature or anime
• Body image to the avatar
• Colorization of grayscale photos
• Medical scan to a real photo
• Real photo to an artist's painting
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