better readme

1 files changed, 31 insertions, 7 deletions

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 # POT: Python Optimal Transport library
 Python Optimal Transport library
 
-This Python librarie is an open source implementation of several functions that allow to solve optimal transport problems in Python.
+This Python library is an open source implementation of several functions that allow to solve optimal transport problems in Python.
 
 It provides the following solvers:
-* Linear program (LP) OT solver/ Earth Movers Distance (using code from Antoine Rolet and Nicolas Bonneel).
-* Entropic regularization OT solver (Sinkhorn Knopp ALgorithm)
-* Bregman projection for Wasserstein barycenter and unmixing.
-* Optimal transport for domain adaptation (with group lasso regularization)
-* Conditional gradient and Generalized conditional gradient for regularized OT.
+* Linear program (LP) OT solver/ Earth Movers Distance (using code from Antoine Rolet and Nicolas Bonneel [1]).
+* Entropic regularization OT solver  with Sinkhorn Knopp Algorithm [2].
+* Bregman projections for Wasserstein barycenter [3] and unmixing [4].
+* Optimal transport for domain adaptation with group lasso regularization [5]
+* Conditional gradient and Generalized conditional gradient for regularized OT [5].
 
-Some demonstrations of what can be done are available in the examples folder.
+Some demonstrations (both in Python and Jupyter Notebook Format) are available in the examples folder.
 
 
 ## Installation
@@ -18,5 +18,29 @@ Some demonstrations of what can be done are available in the examples folder.
 
 ## Examples
 
+## Acknowledgements
+
+The main developers of this library are:
+* Rémi Flamary
+* Nicolas Courty
+
+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):
+
+* Gabriel Peyré (Wasserstein Barycenters in Matlab)
+* Nicolas Bonneel ( C++ code for EMD)
+* Antoine Rolet ( Mex file fro EMD )
+* Marco Cuturi (Sinkhorn Knopp in Matlab/Cuda)
 
 ## References
+
+[1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. (2011, December). Displacement interpolation using Lagrangian mass transport. In ACM Transactions on Graphics (TOG) (Vol. 30, No. 6, p. 158). ACM.
+
+[2] Cuturi, M. (2013). Sinkhorn distances: Lightspeed computation of optimal transport. In Advances in Neural Information Processing Systems (pp. 2292-2300).
+
+[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). Iterative Bregman projections for regularized transportation problems. SIAM Journal on Scientific Computing, 37(2), A1111-A1138.
+
+[4] S. Nakhostin, N. Courty, R. Flamary, D. Tuia, T. Corpetti, Supervised planetary unmixing with optimal transport, Whorkshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS), 2016.
+
+[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, "Optimal Transport for Domain Adaptation," in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1
+
+[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882.