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

The Wasserstein Distance and Optimal Transport Map of Gaussian Processes

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

Optimal transport algorithms for Julia

Unsupervised Domain Adaptation for Acoustic Scene Classification with Wasserstein Distance

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

1D Wasserstein Statistical Loss in Pytorch

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

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

Topological Learning for Brain Networks (Annals of Applied Statistics; MICCAI 2021)

Discovering Conservation Laws using Optimal Transport and Manifold Learning

We've applied the Reptile algorithm to our GAN architectures. The peculiarity is the exclusion of G from meta-learning. Surprisingly, everything worked and the research was published in a paper. More details reported on the paper "Towards Latent Space Optimization of GANs Using Meta-Learning" and the thesis (Italian).

Persistence Diagrams in Julia

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

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

Lots of evaluation metrics for the generative adversarial networks in pytorch

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

LAMDA: Label Matching Deep Domain Adaptation - ICML 2021

A python module for fast calculation of the wasserstein distance on tree metrics implemented in C++.

Employing Optimal Transport metrics for Point Cloud Registration