# An Introduction to Variational Autoencoders

**Authors:** Diederik P. Kingma, Max Welling  
**arXiv:** [1906.02691](https://arxiv.org/abs/1906.02691)  
**Date:** 2019-06-06  
**Summary page:** https://www.summarizepaper.com/en/paper/1906.02691v3/

## Key points

- Variational autoencoders (VAEs) are a type of deep learning model
- VAEs can learn high-dimensional probability distributions by mapping data points to a lower-dimensional latent space
- The paper "An Introduction to Variational Autoencoders" provides a comprehensive overview of VAEs and their extensions
- The authors explain the underlying principles of VAEs, including the encoder-decoder architecture and the use of variational inference to approximate the posterior distribution over latent variables
- Important extensions to VAEs such as conditional VAEs, hierarchical VAEs, and adversarial autoencoders are discussed
- Examples are provided on how these models have been applied in various domains such as image generation, text modeling, and drug discovery
- This paper serves as an excellent introduction for researchers interested in using VAEs for deep learning applications.

## Summary

In their paper "An Introduction to Variational Autoencoders," Diederik P. Kingma and Max Welling provide a comprehensive overview of variational autoencoders (VAEs) and their extensions. VAEs are a type of deep learning model that can learn high-dimensional probability distributions by mapping data points to a lower-dimensional latent space. The authors explain the underlying principles of VAEs in detail, including the encoder-decoder architecture and the use of variational inference to approximate the posterior distribution over latent variables. They also discuss important extensions to VAEs such as conditional VAEs, hierarchical VAEs, and adversarial autoencoders. Examples are provided on how these models have been applied in various domains such as image generation, text modeling, and drug discovery. This paper serves as an excellent introduction for researchers interested in using VAEs for deep learning applications.