TensorFlow implementation of Wasserstein GAN (WGAN) with MNIST dataset.
-
Updated
Nov 9, 2020 - Python
TensorFlow implementation of Wasserstein GAN (WGAN) with MNIST dataset.
Wasserstein barycenter research for images
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".
Improving word mover’s distance by leveraging self-attention matrix
Implementation and results from "Beyond GOTEX: Using Multiple Feature Detectors for Better Texture Synthesis"
Pytorch Implementation for Topic Modeling with Wasserstein Autoencoders
Code for our TMLR '24 Journal: MMD-Regularized UOT.
Demonstration of Wasserstein GAN. Using Earth Mover's distance to measure similarity between two distributions
Tensorflow Implementation of Stochastic Wasserstein Autoencoder for Probabilistic Sentence Generation (NAACL 2019).
GANs Implementations in Keras
FML (Francis' Machine-Learnin' Library) - A collection of utilities for machine learning tasks
code for "Determining Gains Acquired from Word Embedding Quantitatively Using Discrete Distribution Clustering" ACL 2017
A materials discovery algorithm geared towards exploring high-performance candidates in new chemical spaces.
Code for the article "Learning to solve inverse problems using Wasserstein loss"
A Python implementation of Monge optimal transportation
The Wasserstein Distance and Optimal Transport Map of Gaussian Processes
DCGAN and WGAN implementation on Keras for Bird Generation
Tensorflow implementation of Wasserstein GAN - arxiv: https://arxiv.org/abs/1701.07875
POT : Python Optimal Transport
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."