Auto-Encoding Variational Bayes (VAE)

Editorial record

Plain-language summary

Kingma and Welling framed generation as learning a latent variable model and approximating its intractable posterior with a neural encoder. Their key move, the reparameterization trick, rewrote random sampling so gradients could flow through it, allowing the encoder and decoder to be trained together by ordinary backpropagation on a single objective (the evidence lower bound). This gave a scalable way to learn continuous latent representations and generate new data, widely used for images and representation learning.