In machine learning, a variational autoencoder (VAE) is an artificial neural network architecture introduced by Diederik P. Kingma and Max Welling. [1] It is part of the families of probabilistic graphical models and variational Bayesian methods. [2]

Variational AutoEncoders (henceforth referred to as VAEs) embody this spirit of progressive deep learning research, using a few clever math manipulations to formulate a model pretty effective at approximating probability distributions.

Mar 4, 2025 · Variational Autoencoders (VAEs) are generative models in machine learning (ML) that create new data similar to the input they are trained on. Along with data generation they also perform common autoencoder tasks like denoising. Like all autoencoders VAEs consist of: Encoder: Learns important patterns (latent variables) from input data.

Jun 19, 2016 · 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.

May 27, 2022 · Variational Auto-Encoder (VAE) is a widely used approach in unsupervised learning for complicated distributions, whose application includes image generation,representation learning and...

This article gives an introduction to the mathematics behind variational auto-encoders, including variational inference in general and practical aspects.

Jun 27, 2023 · Variational Autoencoders (VAEs) play a crucial role in the era of generative deep learning by enabling the generation of high-quality, diverse, and realistic data. VAEs provide a powerful...

Mar 14, 2023 · In this post, we present the mathematical theory behind VAEs, which is rooted in Bayesian inference, and how this theory leads to an emergent autoencoding algorithm. We also discuss the similarities and differences between VAEs and standard autoencoders.

May 28, 2020 · In this post, we are going to cover some of the basic mathematics required to understand Autoencoders, Variational Autoencoders (VAEs), and Vector Quantised Variational Autoencoders (VQ-VAEs).

What is a VAE? A VAE is an autoencoder (AE). An AE is a neural network that is trained to copy its input to its output. Internally, it has a hidden layer whose output \(h\) is referred to as the code, used to represent the input.