Autoencoder

An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning). 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, to generate lower-dimensional embeddings for subsequent use by other machine learning algorithms.^[1]

Variants exist which aim to make the learned representations assume useful properties.^[2] Examples are regularized autoencoders (sparse, denoising and contractive autoencoders), which are effective in learning representations for subsequent classification tasks,^[3] and variational autoencoders, which can be used as generative models.^[4] Autoencoders are applied to many problems, including facial recognition,^[5] feature detection,^[6] anomaly detection, and learning the meaning of words.^[7][8] In terms of data synthesis, autoencoders can also be used to randomly generate new data that is similar to the input (training) data.^[6]