#计算机科学#Efficient Wasserstein Barycenter in MATLAB (for "Fast Discrete Distribution Clustering Using Wasserstein Barycenter with Sparse Support" TSP)
Code for our ICLR19 paper "Wasserstein Barycenters for Model Ensembling", Pierre Dognin, Igor Melnyk, Youssef Mroueh, Jarret Ross, Cicero Dos Santos, Tom Sercu
This repo contains the implementation of the Wasserstein Barycenter Transport proposed in "Wasserstein Barycenter Transport for Acoustic Adaptation" at ICASSP21 and "Wasserstein Barycenter for Multi-S...
#计算机科学#PyTorch implementation of the paper "Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization" (ICLR 2021)
Implementation of the paper 'Stochastic Wasserstein Barycenters'
Learning Graphons via Structured Gromov-Wasserstein Barycenters
https://arxiv.org/abs/2007.04462
Wasserstein Auto-Encoders
Conditional Wasserstein GANs
#计算机科学#Approximating Wasserstein distances with PyTorch
Code for reproducing experiments in "Improved Training of Wasserstein GANs"
Pytorch implementation of Wasserstein GANs with Gradient Penalty
MMCV for Normalized Wasserstein Distance
Tensorflow implementation of Wasserstein GAN - arxiv: https://arxiv.org/abs/1701.07875
Tensorflow Implementation of Wasserstein GAN (and Improved version in wgan_v2)
Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation. In CVPR 2019.
TensorFlow 2.0 implementation of Improved Training of Wasserstein GANs
Tensorflow Implementation of Paper "Improved Training of Wasserstein GANs"
Official code for "A Normalized Gaussian Wasserstein Distance for Tiny Object Detection"
Keras implementation of Wasserstein GAN. Modified from the ACGAN example.
A matlab toolbox to perform Wasserstein Dictionary Learning or NMF
Pytorch implementation of Improved Training of Wasserstein GANs or WGAN-GP (Wasserstein GAN with Gradient Penalty) using DCGAN architecture for generating 64x64 images.
Implementation of our paper "Wasserstein Adversarial Transformer for Cloud Workload Prediction"
Personalized Purchase Prediction of Market Baskets with Wasserstein-Based Sequence Matching