An auto-encoder is an artificial neural network used for learning efficient codings.^[1] The aim of an auto-encoder is to learn a compressed representation (encoding) for a set of data. This means it is being used for dimensionality reduction. Auto-encoders use three or more 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 number of considerably smaller hidden layers, 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, or only a single sigmoid hidden layer, then the optimal solution to an auto-encoder is strongly related to PCA.^[2]

Auto-encoders can also be used to learn overcomplete feature representations of data.

Training[edit]

An auto-encoder is often trained using one of the many backpropagation variants (conjugate gradient method, steepest descent, etc.) Though often reasonably effective, there are fundamental problems with using backpropagation to train networks with many hidden layers. Once the errors get backpropagated to the first few layers, they are minuscule, and quite ineffectual. This causes the network to almost always learn to reconstruct the average of all the training data. Though more advanced backpropagation methods (such as the conjugate gradient method) help with this to some degree, it still results in very slow learning and poor solutions. This problem is remedied by using initial weights that approximate the final solution. The process to find these initial weights is often called pretraining.

A pretraining technique developed by Geoffrey Hinton for training many-layered "deep" auto-encoders involves treating each neighboring set of two layers like a restricted Boltzmann machine for pre-training to approximate a good solution and then using a backpropagation technique to fine-tune.^[3]

References[edit]

See also[edit]

• Restricted Boltzmann machine