This is a monograph (it is 86 pages) on variational autoencoders. The goal of this work is:

The framework of variational autoencoders (VAEs) (Kingma and Welling, 2014; Rezende et al., 2014) provides a principled method for jointly learning deep latent-variable models and corresponding inference models using stochastic gradient descent. The framework has a wide array of applications from generative modeling, semi-supervised learning to representation learning.

This work is meant as an expanded version of our earlier work (Kingma and Welling, 2014), allowing us to explain the topic in finer detail and to discuss a selection of important follow-up work. This is not aimed to be a comprehensive review of all related work. We assume that the reader has basic knowledge of algebra, calculus and probability theory.

Below is the abstract of An Introduction to Variational Autoencoders.

Variational autoencoders provide a principled framework for learning deep latent-variable models and corresponding inference models. In this work, we provide an introduction to variational autoencoders and some important extensions.

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