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Autoencoder

Neural network that learns efficient data encoding in an unsupervised manner / From Wikipedia, the free encyclopedia

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An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning).[1] The encoding is validated and refined by attempting to regenerate the input from the encoding. The autoencoder learns a representation (encoding) for a set of data, typically for dimensionality reduction, by training the network to ignore insignificant data (“noise”).

Variants exist, aiming to force the learned representations to assume useful properties.[2] Examples are regularized autoencoders (Sparse, Denoising and Contractive), which are effective in learning representations for subsequent classification tasks,[3] and Variational autoencoders, with applications as generative models.[4] Autoencoders are applied to many problems, including facial recognition,[5] feature detection,[6] anomaly detection and acquiring the meaning of words.[7][8] Autoencoders are also generative models which can randomly generate new data that is similar to the input data (training data).[6]