Tutorial on Variational Autoencoders

TL;DR

This tutorial explains the principles, mathematics, and empirical behavior of Variational Autoencoders (VAEs), a popular neural network-based approach for unsupervised learning of complex data distributions, with no prior Bayesian knowledge required.

Contribution

It provides a comprehensive introduction to VAEs, covering their intuition, mathematical foundation, and empirical observations, making the topic accessible to newcomers.

Findings

VAEs can generate complex data like images and scenes.

They are trained efficiently using stochastic gradient descent.

VAEs demonstrate promising results in unsupervised learning tasks.

Abstract

In just three years, Variational Autoencoders (VAEs) have emerged as one of the most popular approaches to unsupervised learning of complicated distributions. VAEs are appealing because they are built on top of standard function approximators (neural networks), and can be trained with stochastic gradient descent. VAEs have already shown promise in generating many kinds of complicated data, including handwritten digits, faces, house numbers, CIFAR images, physical models of scenes, segmentation, and predicting the future from static images. This tutorial introduces the intuitions behind VAEs, explains the mathematics behind them, and describes some empirical behavior. No prior knowledge of variational Bayesian methods is assumed.