Variational Autoencoders #autoencoder

Variational autoencoders (VAEs) are specific types of autoencoders that learn to encode input data into a latent space involving probability, "organizing the space".

This allows for the generation of new data samples that are consistent and useful.

Unlike traditional autoencoders, which aim to map inputs to a deterministic latent space, VAEs learn a distribution over the latent variables.

This lets VAEs generate high quality data samples of new data, while standard autoencoders only try to reconstruct inputs with high accuracy.

C: deepia

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