”wasserstein-barycenter“ 的搜索结果

#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

(ICASSP'21/CVPR'21) Wasserstein Barycenter Transport

#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

Scalable Bayes via Barycenter in Wasserstein Space

https://arxiv.org/abs/2007.04462

Fast discrete distributions clustering using Wasserstein barycenter with sparse support

Wasserstein Auto-Encoders

Conditional Wasserstein GANs

#计算机科学#Approximating Wasserstein distances with PyTorch

PyTorch implementation of Wasserstein Auto-Encoders

Wasserstein DCGAN in Tensorflow/Keras

Code for reproducing experiments in "Improved Training of Wasserstein GANs"

Pytorch implementation of Wasserstein GANs with Gradient Penalty

A pytorch implementation of Paper "Improved Training of Wasserstein GANs"

MMCV for Normalized Wasserstein Distance

Implementation of the Sliced Wasserstein Autoencoder using PyTorch

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

#计算机科学#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"