In this chapter, we will study a class of neural networks known as autoencoders, which have gained traction in recent years. In particular, the ability of autoencoders to remove noise from images has been greatly studied. In this chapter, we will build and train an autoencoder that is able to denoise and restore corrupted images.
In this chapter, we'll cover the following topics:
- What are autoencoders?
- Unsupervised learning
- Types of autoencoders—basic autoencoders, deep autoencoders and convolutional autoencoders
- Autoencoders for image compression
- Autoencoders for image denoising
- Step-by-step guide to build and train an autoencoder in Keras
- Analysis of our results
