Wasserstein barycenter research for images
-
Updated
Oct 13, 2018 - Python
Wasserstein barycenter research for images
TensorFlow implementation of Wasserstein GAN (WGAN) with MNIST dataset.
Optimal transport for comparing short brain connectivity between individuals | Optimal transport | Wasserstein distance | Barycenter | K-medoids | Isomap| Sulcus | Brain
Code for "Fixed Support Tree-Sliced Wasserstein Barycenter"
Python package for the ICML 2022 paper "Unsupervised Ground Metric Learning Using Wasserstein Singular Vectors".
MXNet/Gluon implementation of Wasserstein Auto-Encoders (WAE)
Improving word mover’s distance by leveraging self-attention matrix
Sparse simplex projection-based Wasserstein k-means
Implementation and results from "Beyond GOTEX: Using Multiple Feature Detectors for Better Texture Synthesis"
Employing Optimal Transport metrics for Point Cloud Registration
Generating Atari Images with WGANs in PyTorch
Pytorch Implementation for Topic Modeling with Wasserstein Autoencoders
Code for our TMLR '24 Journal: MMD-Regularized UOT.
Source code for "Training Generative Adversarial Networks Via Turing Test".
Julia interface for the Python Optimal Transport (POT) library
Demonstration of Wasserstein GAN. Using Earth Mover's distance to measure similarity between two distributions
Variational Optimal Transportation
Source code for the ICML2019 paper "Subspace Robust Wasserstein Distances"
Optimal Transport and Optimization related experiments.
Add a description, image, and links to the wasserstein topic page so that developers can more easily learn about it.
To associate your repository with the wasserstein topic, visit your repo's landing page and select "manage topics."