Skip to content
master
Switch branches/tags
Code

Latest commit

 

Git stats

Files

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

Flat Metric Minimization

This repo contains a minimal PyTorch implementation to reproduce Fig. 6 and Fig. 7 from the paper:

Flat Metric Minimization with Applications in Generative Modeling (Thomas Möllenhoff, Daniel Cremers; ICML 2019). https://arxiv.org/abs/1905.04730

Notes

  • We have tested the code on: Ubuntu 16.04; Python 3.7.1; PyTorch 1.0.0
  • Running the MNIST example (demo_mnist.py) will first download the MNIST dataset into the data/ folder
  • The results will be saved in results/2d (for demo_2d.py) and results/mnist (for demo_mnist.py)

Demo outputs

python demo_2d.py --k 0

python demo_2d.py --k 1

python demo_mnist.py 

Publication

@article{flatgan,
    title = {Flat Metric Minimization with Applications in Generative Modeling},
    author={Thomas Möllenhoff, Daniel Cremers},
    journal={International Conference on Machine Learning},
    year={2019},
    url={https://arxiv.org/abs/1905.04730}
}

About

PyTorch implementation of paper "Flat Metric Minimization with Applications in Generative Modeling"

Resources

License

Releases

No releases published

Packages

No packages published

Languages