7 Image generation with variational autoencoders
- Autoencoders vs. variational autoencoders
- Building and training an Autoencoder toreconstruct handwritten digits
- Building and training a variational autoencoder to generate human face images
- Performing encoding arithmetic and interpolation with a trained variational autoencoder
So far, you have learned how to generate shapes, numbers, and images, all by using generative adversarial networks (GANs). In this chapter, you’ll learn to create images by using another generative model: variational autoencoders (VAEs). You’ll also learn the practical uses of VAEs by performing encoding arithmetic and encoding interpolation.
7.1 An overview of AEs
7.1.1 What is an AE?
7.1.2 Steps in building and training an AE
7.2 Building and training an AE to generate digits
7.2.1 Gathering handwritten digits
7.2.2 Building and training an AE
7.2.3 Saving and using the trained AE
7.3 What are VAEs?
7.3.1 Differences between AEs and VAEs
7.3.2 The blueprint to train a VAE to generate human face images
7.4 A VAE to generate human face images
7.4.1 Building a VAE
7.4.2 Training the VAE
7.4.3 Generating images with the trained VAE
7.4.4 Encoding arithmetic with the trained VAE
Summary