Variational autoencoder - Wikipedia In machine learning, a variational autoencoder (VAE) is an artificial neural network architecture introduced by Diederik P Kingma and Max Welling in 2013 [1]
变分自编码器（Variational Autoencoders，VAE）详解：数学原理、图示、代码 Perhaps the greatest contribution of the VAE framework is the realization that we can counteract this variance by using what is now known as the “reparameterization trick”, a simple procedure to reorganize our gradient computation that reduces variance in the gradients