Carl Doersch, “Tutorial on Variational Autoencoders.” arXiv, January 2021 [Online]. Available at: http://arxiv.org/abs/1606.05908. [Accessed: August 28, 2023]
@misc{doersch_tutorial_2021,
title = {Tutorial on {Variational} {Autoencoders}},
url = {http://arxiv.org/abs/1606.05908},
language = {en},
urldate = {2023-08-28},
publisher = {arXiv},
author = {Doersch, Carl},
month = jan,
year = {2021},
arxiv = {arXiv:1606.05908 [cs, stat]},
arxivdoi = {https://doi.org/10.48550/arXiv.1606.05908},
keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
code = {https://github.com/cdoersch/vae_tutorial},
tldr = {Published 2016 with edits in 2021. Tutorial on Variational Autoencoders, reparameterization trick, and Conditional Variational Autoencoders. Examples using MNIST. Does not assume prior knowledge of variational Bayesian methods.}
}
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 [1, 2], faces [1, 3, 4], house numbers [5, 6], CIFAR images [6], physical models of scenes [4], segmentation [7], and predicting the future from static images [8]. 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.
tl;dr: Published 2016 with edits in 2021. Tutorial on Variational Autoencoders, reparameterization trick, and Conditional Variational Autoencoders. Examples using MNIST. Does not assume prior knowledge of variational Bayesian methods.
arXiv: http://doi.org/https://doi.org/10.48550/arXiv.1606.05908
h/t Amy Tabbcode: https://github.com/cdoersch/vae_tutorial