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Variational Autoencoder (VAE)

A Variational Autoencoder is a type of generative model in machine learning that uses neural networks to encode data into a latent, compressed representation, and then decode it back into its original form.

How VAEs Function

VAEs are especially useful in tasks where the goal is to generate new data points that are similar to the input data, like image or text generation. They work by approximating the probability distribution of the input data and sampling from this distribution to generate new data points.

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