An autoencoder is a type of neural network that is trained to attempt to copy its input to its output. It has a hidden layer (let's call it h) that describes a code used to represent the input. The network may be viewed as consisting of two parts:
- Encoder function: h = f (x)
- Decoder that produces a reconstruction: r = g(h)
The following figure shows a basic autoencoder with input n and a hidden layer with neurons m:
Basic representation of an autoencoder
Autoencoders are designed to be unable to learn to copy perfectly. They are restricted in ways that allow them to copy only approximately, and to copy only input that resembles ...
