Autoencoder

An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning).^[1][2] An autoencoder learns two functions: an encoding function that transforms the input data, and a decoding function that recreates the input data from the encoded representation. The autoencoder learns an efficient representation (encoding) for a set of data, typically for dimensionality reduction.

Variants exist, aiming to force the learned representations to assume useful properties.^[3] Examples are regularized autoencoders (Sparse, Denoising and Contractive), which are effective in learning representations for subsequent classification tasks,^[4] and Variational autoencoders, with applications as generative models.^[5] Autoencoders are applied to many problems, including facial recognition,^[6] feature detection,^[7] anomaly detection and acquiring the meaning of words.^[8][9] Autoencoders are also generative models which can randomly generate new data that is similar to the input data (training data).^[7]