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
PyTorch VAE - GitHub A collection of Variational AutoEncoders (VAEs) implemented in pytorch with focus on reproducibility The aim of this project is to provide a quick and simple working example for many of the cool VAE models out there All the models are trained on the CelebA dataset for consistency and comparison
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]
Variational Autoencoders: How They Work and Why They Matter Unlike traditional autoencoders that produce a fixed point in the latent space, the encoder in a VAE outputs parameters of a probability distribution—typically the mean and variance of a Gaussian distribution This allows the VAE to model data uncertainty and variability effectively
Bidirectional Variational Autoencoders The VAE includes a new recognition (or encoding) model q(z|x, φ) that approximates the intractable likelihood q(z|x, θ) The probability q(z|x, φ) represents a probabilistic encoder while p(x|z, θ) represents a probabilistic decoder