am-official/autoencoders

Implementation of simple autoencoders networks with Keras

Autoencoders

Autoencoders (AE) are neural networks that aims to copy their inputs to their outputs. They work by compressing the input into a latent-space representation, and then reconstructing the output from this representation. This kind of network is composed of two parts :

1. Encoder: This is the part of the network that compresses the input into a latent-space representation. It can be represented by an encoding function h=f(x).
2. Decoder: This part aims to reconstruct the input from the latent space representation. It can be represented by a decoding function r=g(h).

This notebook show the implementation of five types of autoencoders :

• Vanilla Autoencoder
• Multilayer Autoencoder
• Convolutional Autoencoder
• Regularized Autoencoder
• Variational Autoencoder

The explanation of each (except VAE) can be found here