Generative Adversarial Networks for Synthesizing New Data
In the previous chapter, we focused on recurrent neural networks for modeling sequences. In this chapter, we will explore generative adversarial networks (GANs) and see their application in synthesizing new data samples. GANs are considered to be the most important breakthrough in deep learning, allowing computers to generate new data (such as new images).
In this chapter, we will cover the following topics:
- Introducing generative models for synthesizing new data
- Autoencoders, variational autoencoders (VAEs), and their relationship to GANs
- Understanding the building blocks of GANs
- Implementing a simple GAN model to generate handwritten digits
- Understanding transposed convolution and batch normalization (BatchNorm or BN)
- Improving GANs: deep convolutional GANs and GANs using the Wasserstein distance
