Official code for "A Normalized Gaussian Wasserstein Distance for Tiny Object Detection"

Optimal transport algorithms for Julia

PyTorch implementation of slicing adversarial network (SAN)

Measure the distance between two spectra/signals using optimal transport and related metrics

The Wasserstein Distance and Optimal Transport Map of Gaussian Processes

A materials discovery algorithm geared towards exploring high-performance candidates in new chemical spaces.

Source code for the ICML2019 paper "Subspace Robust Wasserstein Distances"

Functional Optimal Transport: Map Estimation and Domain Adaptation for Functional data

1D Wasserstein Statistical Loss in Pytorch

A thorough review of the paper "Learning Embeddings into Entropic Wasserstein Spaces" by Frogner et al. Includes a reproduction of the results on word embeddings.

Multi-Source Domain Adaptation via Optimal Transport for Student-Teacher Learning - UAI 2021

Unsupervised Domain Adaptation for Acoustic Scene Classification with Wasserstein Distance

LAMDA: Label Matching Deep Domain Adaptation - ICML 2021

Discovering Conservation Laws using Optimal Transport and Manifold Learning

Persistence Diagrams in Julia

My version of cWGAN-gp. Simply my cDCGAN-based but using the Wasserstein Loss and gradient penalty.

Header only C++ implementation of the Wasserstein distance (or earth mover's distance)

Sparse simplex projection-based Wasserstein k-means

This Repository Contains Solution to the Assignments of the Generative Adversarial Networks (GANs) Specialization from deeplearning.ai on Coursera Taught by Sharon Zhou, Eda Zhou, Eric Zelikman

OT1D: Discrete Optimal Transport in 1D by Linear Programming