A variational autoencoder encodes data into a chosen distribution, such as a normal or beta distribution, and decodes it back, compressing it into a small latent representation.

“into like a a compact latent. At the first idea of this was like a variational auto-encoder. A variational auto-encoder says, well, you have to be able to map the data to a normal distribution or a beta distribution, whatever you you set in the middle. And then you have to be able to map this back. And effectively this compresses everything down into this uh this is tiny gate in the middle.”