Skip to content
master
Switch branches/tags
Code

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 
 
 

swgmm

This repository contains a demo implementation of the method described in:

"Sliced Wasserstein Distance for Learning Gaussian Mixture Models", CVPR'18

which defines the sliced-Wasserstein means problem, and describes a novel technique for fitting Gaussian Mixture Models to data. In short, the method minimizes the sliced-Wasserstein distance between the data distribution and a GMM with respect to the GMM parameters.

About

Sliced Wasserstein Distance for Learning Gaussian Mixture Models

Resources

Releases

No releases published

Packages

No packages published