Unpaired image translation with CycleGAN
CycleGAN was created by the same research group who invented pix2pix. CycleGAN could train with unpaired images using two generators and two discriminators. However, by using pix2pix as a foundation, CycleGAN is actually quite simple to implement once you understand how the cycle consistency loss works. Before this, let's try to understand the advantage of CycleGAN over pix2pix in the following sections.
Unpaired dataset
One drawback of pix2pix is that it requires a paired training dataset. For some applications, we can create a dataset rather easily. A grayscale-to-color images dataset and vice-versa is probably the simplest to create using any image processing software libraries such as OpenCV or Pillow. Similarly, we could also easily create sketches from real images using edge detection techniques. For a photo-to-artistic-painting dataset, we can use neural style transfer (we'll cover this in Chapter 5, Style Transfer) to...
