In this chapter, we looked at autoencoders, a class of neural networks that learn the latent representation of input images. We saw that all autoencoders have an encoder and decoder component. The role of the encoder is to encode the input to a learned, compressed representation and the role of the decoder is to reconstruct the original input using the compressed representation.
We first looked at autoencoders for image compression. By training an autoencoder with identical input and output, the autoencoder learns the most salient features of the input. Using MNIST images, we constructed an autoencoder with a 24.5 times compression rate. Using this learned 24.5x compressed representation, the autoencoder is able to successfully reconstruct the original input.
Next, we looked at denoising autoencoders. By training an autoencoder with noisy images as input and clean images...
