Autoencoder

From Wikipedia, the free encyclopedia

An auto-encoder is an artificial neural network used for learning efficient codings. The aim of an auto-encoder is to learn a compressed representation (encoding) for a set of data. Auto-encoders use three layers:

• An input layer. For example, in a face recognition task, the neurons in the input layer could map to pixels in the photograph.
• A considerably smaller hidden layer, which will form the encoding.
• An output layer, where each neuron has the same meaning as in the input layer.

If linear neurons are used, then an auto-encoder is very similar to PCA.

[edit] External links

• Presentation introducing auto-encoders for number recognition
• Reducing the Dimensionality of Data with Neural Networks (Science, 28 July 2006, Hinton & Salakhutdinov)