[MRG] Sliced wasserstein (#203)

* example for log treatment in bregman.py

* Improve doc

* Revert "example for log treatment in bregman.py"

This reverts commit 9f51c14e

* Add comments by Flamary

* Delete repetitive description

* Added raw string to avoid pbs with backslashes

* Implements sliced wasserstein

* Changed formatting of string for py3.5 support

* Docstest, expected 0.0 and not 0.

* Adressed comments by @rflamary

* No 3d plot here

* add sliced to the docs

* Incorporate comments by @rflamary

* add link to pdf

Co-authored-by: Rémi Flamary <remi.flamary@gmail.com>

1 files changed, 4 insertions, 0 deletions

diff --git a/README.md b/README.md
index e3598f1..6fe528a 100644
--- a/README.md
+++ b/README.md
@@ -33,6 +33,7 @@ POT provides the following generic OT solvers (links to examples):
 * [Unbalanced OT](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_UOT_1D.html) with KL relaxation and [barycenter](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_UOT_barycenter_1D.html) [10, 25].
 * [Partial Wasserstein and Gromov-Wasserstein](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_partial_wass_and_gromov.html) (exact [29] and entropic [3]
   formulations).
+* [Sliced Wasserstein](https://pythonot.github.io/auto_examples/sliced-wasserstein/plot_variance.html) [31, 32].
 
 POT provides the following Machine Learning related solvers:
 
@@ -180,6 +181,7 @@ The contributors to this library are
 * [Romain Tavenard](https://rtavenar.github.io/) (1d Wasserstein)
 * [Mokhtar Z. Alaya](http://mzalaya.github.io/) (Screenkhorn)
 * [Ievgen Redko](https://ievred.github.io/) (Laplacian DA, JCPOT)
+* [Adrien Corenflos](https://adriencorenflos.github.io/) (Sliced Wasserstein Distance)
 
 This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various languages):
 
@@ -263,3 +265,5 @@ You can also post bug reports and feature requests in Github issues. Make sure t
 [29] Chapel, L., Alaya, M., Gasso, G. (2019). [Partial Gromov-Wasserstein with Applications on Positive-Unlabeled Learning](https://arxiv.org/abs/2002.08276), arXiv preprint arXiv:2002.08276.
 
 [30] Flamary R., Courty N., Tuia D., Rakotomamonjy A. (2014). [Optimal transport with Laplacian regularization: Applications to domain adaptation and shape matching](https://remi.flamary.com/biblio/flamary2014optlaplace.pdf), NIPS Workshop on Optimal Transport and Machine Learning OTML, 2014.
+
+[31] Bonneel, Nicolas, et al. [Sliced and radon wasserstein barycenters of measures](https://perso.liris.cnrs.fr/nicolas.bonneel/WassersteinSliced-JMIV.pdf), Journal of Mathematical Imaging and Vision 51.1 (2015): 22-45