Chapter 2: Variational Autoencoder
In the previous chapter, we looked at how a computer sees an image as pixels, and we devised a probabilistic model for pixel distribution for image generation. However, this is not the most efficient way to generate an image. Instead of scanning an image pixel by pixel, we first look at the image and try to understand what is inside. For example, a girl is sitting, wearing a hat, and smiling. Then we use that information to draw a portrait. This is how autoencoders work.
In this chapter, we will first learn how to use an autoencoder to encode pixels into latent variables that we can sample from to generate images. Then we will learn how to tweak it to create a more powerful model known as a variational autoencoder (VAE). Finally, we will train our VAE to generate faces and perform face editing. The following topics will be covered in this chapter:
- Learning latent variables with autoencoders
- Variational autoencoders
- Generating faces...
