Summary

We began this chapter by learning how the basic cGAN enforces the class label as a condition to generate MNIST. We implemented two different ways of injecting the condition, one being to one-hot encode the class labels to a dense layer, reshape them to match the channel dimensions of the input noise, and then concatenate them together. The other way is to use the embedding layer and element-wise multiplication.

Next, we learned to implement pix2pix, a special type of condition GAN for image-to-image translation. It uses PatchGAN as a discriminator, which looks at patches of images to encourage fine details or high-frequency components in the generated image. We also learned about a popular network architecture, U-Net, that has been used for various applications. Although pix2pix can generate high-quality image translation, the image is one-to-one mapping without diversification of the output. This is due to the removal of input noise. This was overcome by BicycleGAN, which...