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This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and machine learning.
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It provides the following solvers:
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* Optimal transport for domain adaptation with group lasso regularization [5]
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* Conditional gradient [6] and Generalized conditional gradient for regularized OT [7].
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We are also currently working on the following features:
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[] Image color adaptation demo
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[] Scikit-learn inspired classes for domain adaptation
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[] Mapping estimation as proposed in [8]
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Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder.
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## Installation
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Here is a list of the Python notebook if you want a quick look:
*[OT with user provided regularization](https://github.com/rflamary/POT/blob/master/examples/Demo_Optim_OTreg.ipynb)
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## Acknowledgements
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[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882.
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[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized conditional gradient: analysis of convergence and applications. arXiv preprint arXiv:1510.06567.
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[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for discrete optimal transport", Neural Information Processing Systems (NIPS), 2016.
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