optimal-transport

A collection of AWESOME things about domian adaptation

POT : Python Optimal Transport

A Python library for common tasks on 3D point clouds

Approximating Wasserstein distances with PyTorch

Optimal Transport tools implemented with the JAX framework, to get auto-diff, parallel and jit-able computations.

Neural Architecture Search with Bayesian Optimisation and Optimal Transport

A software package for analyzing snapshots of developmental processes

Implementation of the Sliced Wasserstein Autoencoder using PyTorch

Implementation of the Sliced Wasserstein Autoencoders

CVPR 2020, Semantic Correspondence as an Optimal Transport Problem, Pytorch Implementation.

Optimal transport algorithms for Julia

Capsule research with our trivial contribution

Dynamic Time Warping (DTW) and related algorithms in Julia, at Julia speeds

Unofficial PyTorch implementation of "Progressive Growing of GANs for Improved Quality, Stability, and Variation".

Sinkhorn Label Allocation is a label assignment method for semi-supervised self-training algorithms. The SLA algorithm is described in full in this ICML 2021 paper: https://arxiv.org/abs/2102.08622.

A Python implementation of Monge optimal transportation

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

PyTorch implementation of "Wasserstein-2 Generative Networks" (ICLR 2021)

Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport

Continuous-time gradient flow for generative modeling and variational inference

Learned string similarity for entity names using optimal transport.

FML (Francis' Machine-Learnin' Library) - A collection of utilities for machine learning tasks

Unofficial PyTorch implementation of "GAN-QP: A Novel GAN Framework without Gradient Vanishing and Lipschitz Constraint"

The repository contains reproducible PyTorch source code of our paper Generative Modeling with Optimal Transport Maps, ICLR 2022.

Official Implementation of AlignMixup - CVPR 2022

A set of tests for evaluating large-scale algorithms for Wasserstein-2 transport maps computation (NeurIPS 2021)

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

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.

The Wasserstein Distance and Optimal Transport Map of Gaussian Processes

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