The Variational Autoencoder (VAE) is a not-so-new-anymore Latent Variable Model (Kingma & Welling, 2014), which by introducing a probabilistic interpretation of autoencoders, allows to not only estimate the variance/uncertainty in the predictions, but also to inject domain knowledge through the use of informative priors, and possibly to make the latent space more interpretable. VAEs can have various applications, mostly related to data generation (for example, image generation, sound gene…
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