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AI & Machine Learning · Advanced

Variational Autoencoder (VAE)

A generative autoencoder that learns a probabilistic latent space from which new samples can be drawn.

Definition

VAEs encode inputs as distributions (mean and variance) rather than points, regularizing the latent space so sampling produces realistic outputs. Latent diffusion models such as Stable Diffusion use a VAE to move between pixel space and latent space.

Exam tip

A VAE is generative because it samples from a learned latent distribution; a plain autoencoder is not designed for sampling.

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