Let us tackle a simple problem that CycleGAN can address. In Chapter 3, Autoencoders, we used an autoencoder to colorize grayscale images from the CIFAR10 dataset. We can recall that the CIFAR10 dataset is made of 50,000 trained data and 10,000 test data samples of 32 × 32 RGB images belonging to ten categories. We can convert all color images into grayscale using rgb2gray(RGB) as discussed in Chapter 3, Autoencoders.
Following on from that, we can use the grayscale train images as source domain images and the original color images as the target domain images. It's worth noting that although the dataset is aligned, the input to our CycleGAN is a random sample of color images and a random sample of grayscale images. Thus, our CycleGAN will not see the train data as aligned. After training, we'll use the test grayscale images to observe the performance of the CycleGAN:
Figure 7.1.6: The forward cycle generator G, implementation in Keras...
