Wasserstein barycenter research for images
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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
Python package for the ICML 2022 paper "Unsupervised Ground Metric Learning Using Wasserstein Singular Vectors".
Code for "Fixed Support Tree-Sliced Wasserstein Barycenter"
MXNet/Gluon implementation of Wasserstein Auto-Encoders (WAE)
Improving word mover’s distance by leveraging self-attention matrix
Implementation and results from "Beyond GOTEX: Using Multiple Feature Detectors for Better Texture Synthesis"
Sparse simplex projection-based Wasserstein k-means
Employing Optimal Transport metrics for Point Cloud Registration
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".
Demonstration of Wasserstein GAN. Using Earth Mover's distance to measure similarity between two distributions
Variational Optimal Transportation
Julia interface for the Python Optimal Transport (POT) library
Optimal Transport and Optimization related experiments.
Source code for the ICML2019 paper "Subspace Robust Wasserstein Distances"
Torch implementation of Wasserstein GAN https://arxiv.org/abs/1701.07875
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