Autoencoder is an artificial neural network used for unsupervised learning of efficient codings. The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for the purpose of dimensionality reduction. Recently, the autoencoder concept has become more widely used for learning generative models of data. the simplest form of an autoencoder is a feedforward, non-recurrent neural network very similar to the multilayer perceptron (MLP) – having an input layer, an output layer and one or more hidden layers connecting them –, but with the output layer having the same number of nodes as the input layer, and with the purpose of reconstructing its own inputs (instead of predicting the target value given inputs ). Therefore, autoencoders are unsupervised learning models. Various techniques exist to prevent autoencoders from learning the identity function and to improve their ability to capture important information and learn richer representations, for example, denoising autoencoders which take a partially corrupted input whilst training to recover the original undistorted input; sparse autoencoders which allow sparse representations of inputs useful in pretraining for classification tasks; variational autoencoder which models inherit autoencoder architecture, but makes strong assumptions concerning the distribution of latent variables, etc.
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