Skip to content

sedelmeyer/wasserstein-auto-encoder

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

74 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

An Introduction to the Wasserstein auto-encoder

This repository contains a brief tutorial inspired by the paper "Wasserstein Auto-Encoders" by Tolstikhin, Bousquet, Gelly & Schölkopf (2017)

In this tutorial, we compare model frameworks for the generative adversarial network (GAN) formulation of the Wasserstein auto-encoder (WAEgan), the basic non-stochastic auto-encoder (AE), and the variational auto-encoder (VAE). To accomplish this, we implement each model in PyTorch as a convolutional auto-encoder similar to the popular DCGAN model and compare results with the MNIST and FashionMNIST datasets.

Contributors:

About

A brief tutorial on the Wasserstein auto-encoder

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published