Web Reference: Oct 15, 2014 · Both PCA and autoencoder can do demension reduction, so what are the difference between them? In what situation I should use one over another? Jan 6, 2018 · 37 As per this and this answer, autoencoders seem to be a technique that uses neural networks for dimension reduction. I would like to additionally know what is a variational autoencoder (its main differences/benefits over a "traditional" autoencoders) and also what are the main learning tasks these algorithms are used for. Jan 22, 2018 · The standard autoencoder can be illustrated using the following graph: As stated in the previous answers it can be viewed as just a nonlinear extension of PCA. But compared to the variational autoencoder the vanilla autoencoder has the following drawback: The fundamental problem with autoencoders, for generation, is that the latent space they convert their inputs to and where they're encoded ...
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