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author | Rémi Flamary <remi.flamary@gmail.com> | 2016-10-27 16:24:58 +0200 |
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committer | Rémi Flamary <remi.flamary@gmail.com> | 2016-10-27 16:24:58 +0200 |
commit | 0b1667deeef0640e0fcb7f125d0fb1658306b475 (patch) | |
tree | 40187608b02c4e466d43f98050082e7742b7f5df | |
parent | 6bc9f4db4dda573c94506d632c48f0cd0f78c66d (diff) |
better readme
-rw-r--r-- | README.md | 38 |
1 files changed, 31 insertions, 7 deletions
@@ -1,16 +1,16 @@ # 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. |