LSMI-Sinkhorn: Semi-supervised Mutual Information Estimation with Optimal Transport

This is the implementation of our ECML/PKDD21 paper LSMI-Sinkhorn: Semi-supervised Mutual Information Estimation with Optimal Transport by Liu Y., Yamada M., Tsai YH., Le T., Salakhutdinov R., Yang Y.

Requirements

pytorch
numpy
PIL

SMI Estimation on Synthetic Data

python run_synthetic_exps.py

The results are in "synthetic_result" folder

Image Summarization Experiments

First, download the images from http://users.sussex.ac.uk/~nq28/kernelized_sorting.html, unzip and put the "images" folder under this codebase. Then, run:

python main_layout_ECML-PKDD.py

The result layout images are in "layout" folder.

Bibtex

If you use this code or results for your research, please consider citing:

@inproceedings{liu2019lsmi,
  title={LSMI-Sinkhorn: Semi-supervised Mutual Information Estimation with Optimal Transport},
  author={Liu, Yanbin and Yamada, Makoto and Tsai, Yao-Hung Hubert and Le, Tam and Salakhutdinov, Ruslan and Yang, Yi},
  booktitle={ECML/PKDD},
  year={2021}
}