Chapter 4: Image-to-Image Translation
In part one of the book, we learned to generate photorealistic images with VAE and GANs. The generative models can turn some simple random noise into high-dimensional images with complex distribution! However, the generation processes are unconditional, and we have fine control over the images to be generated. If we use MNIST as an example, we will not know which digit will be generated; it is a bit of a lottery. Wouldn't it be nice to be able to tell GAN what we want it to generate? This is what we will learn in this chapter.
We will first learn to build a conditional GAN (cGAN) that allows us to specify the class of images to generate. This lays the foundation for more complex networks that follow. We ...
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