Summary
We started this chapter by learning how to use an encoder to compress high-dimensional data into low-dimensional latent variables, then use a decoder to reconstruct the data from the latent variables. We learned that the autoencoder's limitation is not being able to guarantee a continuous and uniform latent space, which makes it difficult to sample from. Then we incorporated Gaussian sampling to build a VAE to generate MNIST digits.
Finally, we built a bigger VAE to train on the face dataset and had fun creating and manipulating faces. We learned the importance of the sampling distribution in the latent space, latent space arithmetic, and KLD, which lay the foundation for Chapter 3, Generative Adversarial Network.
Although GANs are more powerful than VAEs in generating photorealistic images, the earlier GANs were difficult to train. Therefore, we will learn about the fundamentals of GANs. By the end of the next chapter, you will have learned the fundamentals of...