POT
A comprehensive Python library for solving optimal transport problems with solvers for linear, entropic, Gromov-Wasserstein, and unbalanced OT, plus machine learning applications.
What is POT?
POT (Python Optimal Transport) is a Python library that provides a comprehensive suite of solvers for optimal transport problems. It enables efficient computation of distances between probability distributions, with applications in machine learning, signal processing, and data analysis. The library implements both classical and cutting-edge OT algorithms, including regularized, unbalanced, and Gromov-Wasserstein variants.
Researchers and practitioners in machine learning, computer vision, and data science who need to measure or optimize distances between distributions, perform domain adaptation, or leverage OT-based methodologies in their workflows.
POT stands out for its extensive collection of OT solvers, strong academic foundation with numerous cited algorithms, and practical multi-backend support for deep learning frameworks. It is the go-to open-source library for OT in Python due to its robustness, active maintenance, and comprehensive documentation.
Overview
POT : Python Optimal Transport
Use Cases
Best For
- Domain adaptation tasks requiring distribution alignment between source and target datasets
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