iamalexkorotin/Wasserstein2Barycenters
PyTorch implementation of the paper "Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization" (ICLR 2021)
This project helps researchers and practitioners in machine learning and data science who need to find an 'average' or 'central' distribution from multiple input distributions, especially when dealing with high-dimensional data like image color palettes. It takes several continuous probability distributions as input and efficiently calculates their Wasserstein-2 barycenter, which represents a geometrically meaningful average. This is particularly useful for scientists working with complex data averaging and transformation tasks.
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Use this if you need to compute a continuous Wasserstein-2 barycenter for high-dimensional distributions, especially for tasks like averaging color palettes or other complex data distributions, and prefer an efficient, non-minimax approach.
Not ideal if your task involves discrete distributions, or if you are not familiar with deep learning frameworks like PyTorch, as the implementation is GPU-based and uses input convex neural networks.
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MIT
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Last pushed
Jun 17, 2022
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