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De-Noising And Contractive Are Examples Of Which Network


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Autoencoders include de-noising and contractive. A de-noising autoencoder can recreate data from a damaged input signal. The removal of some elements of the original data is an example of corruption. An encoder’s output is usually a refined version of the original input. An encoder’s output is usually a refined version of the original input. An unsupervised learning approach used to train deep networks is a contractive autoencoder.


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