There are 2 repositories under wasserstein-distance topic.
Optimal transport algorithms for Julia
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
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.
1D Wasserstein Statistical Loss in Pytorch
Persistence Diagrams in Julia
Discovering Conservation Laws using Optimal Transport and Manifold Learning
Header only C++ implementation of the Wasserstein distance (or earth mover's distance)
Sparse simplex projection-based Wasserstein k-means
Fast Topological Clustering with Wasserstein Distance (ICLR 2022)
Lots of evaluation metrics for the generative adversarial networks in pytorch
Neural Network Time Signal Detection with Wasserstein Loss
Variational Filtering via Wasserstein Gradient Flow
Investigating the Capability of Generative Adversarial Networks in Capturing Implicit Laws in Physical Systems - Master thesis 2023
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).
Earth mover's distance with Python.
Library of Semi-Relaxed Optimal Transport
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
Formulation of a complex-valued deep learning framework based on JAX, and successive application over a vibration signals classification task.
Implementation of some types of GANs (Deep convolutional GAN - Wasserstein GAN - conditional GAN) with PyTorch library
WGAN with feedback from discriminator& LayerNorm instead of BatchNorm
Employing Optimal Transport metrics for Point Cloud Registration