Autoencoder

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. This means it is being used for dimensionality reduction. More specifically, it is a feature extraction method. 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, then an auto-encoder is very similar to PCA. Auto-encoder is used in MediCoder Premium (medical coding and terminology tool) where it encode a large number of terms in an offline situation as well as being used to interactively code an individual term and automatically performs a fuzzy match against historical data and presents this to the user.

A transforming auto-encoder can force the outputs of a capsule to represent any property of an image that we can manipulate in a known way. It is easy, for example, to scale up all of the pixel intensities. If a first-level capsule outputs a number that is first multiplied by the brightness scaling factor and then used to scale the outputs of its generation units when predicting the brightness transformed output, this number will learn to represent brightness and will allow the capsule to disentangle the probability that an instance of its visual entity is present from the brightness of the instance. If the direction of lighting of a scene can be varied in a controlled way, a capsule can be forced to output two numbers representing this direction but only if the visual entity is complex enough to allow the lighting direction to be extracted from the activities of the recognition units.

Training

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.

High-dimensional data can be converted to low-dimensional codes by training a multilayer neural network with a small central layer to reconstruct high-dimensional input vectors. Gradient descent can be used for fine-tuning the weights in such “autoencoder” networks, but this works well only if the initial weights are close to a good solution. There are effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data.

External links

• Reducing the Dimensionality of Data with Neural Networks (Science, 28 July 2006, Hinton & Salakhutdinov)