Alternatives and similar repositories for Large-Scale-OT:

Users that are interested in Large-Scale-OT are comparing it to the libraries listed below

• tvayer / SGW
  Code for Sliced Gromov-Wasserstein
  ☆67Updated 5 years ago
• HongtengXu / Relational-AutoEncoders
  Learning Autoencoders with Relational Regularization
  ☆46Updated 4 years ago
• eifuentes / swae-pytorch
  Implementation of the Sliced Wasserstein Autoencoder using PyTorch
  ☆101Updated 6 years ago
• bunnech / gwgan
  Learning Generative Models across Incomparable Spaces (ICML 2019)
  ☆27Updated 5 years ago
• audeg / Sinkhorn-GAN
  Learning generative models with Sinkhorn Loss
  ☆28Updated 6 years ago
• thibsej / unbalanced_gromov_wasserstein
  Implementation of the Gromov-Wasserstein distance to the setting of Unbalanced Optimal Transport
  ☆44Updated 2 years ago
• mblondel / smooth-ot
  Python implementation of smooth optimal transport.
  ☆57Updated 3 years ago
• francoispierrepaty / SubspaceRobustWasserstein
  Source code for the ICML2019 paper "Subspace Robust Wasserstein Distances"
  ☆28Updated 5 years ago
• kilianFatras / minibatch_Wasserstein
  Code for "Learning with minibatch Wasserstein: asymptotic and gradient properties".
  ☆13Updated 3 years ago
• lchapel / partial-GW-for-PU
  Contains the code relative to the paper Partial Gromov-Wasserstein with Applications on Positive-Unlabeled Learning https://arxiv.org/abs…
  ☆21Updated 5 years ago
• GiulsLu / OT-gradients
  ☆12Updated 6 years ago
• mikigom / large-scale-OT-mapping-TF
  Tensorflow Implementation of "Large-scale Optimal Transport and Mapping Estimation"(ICLR2018/NIPS 2017 OTML)
  ☆19Updated 6 years ago
• jeanfeydy / global-divergences
  MMD, Hausdorff and Sinkhorn divergences scaled up to 1,000,000 samples.
  ☆55Updated 5 years ago
• GiulsLu / Sinkhorn-Barycenters
  Sinkhorn Barycenters via Frank-Wolfe algorithm
  ☆24Updated 5 years ago
• skolouri / swgmm
  Sliced Wasserstein Distance for Learning Gaussian Mixture Models
  ☆62Updated last year
• wgrathwohl / LSD
  Official Release of "Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling"
  ☆49Updated 4 years ago
• kevin-w-li / deep-kexpfam
  ☆28Updated 3 years ago
• audeg / StochasticOT
  Stochastic Optimization for Optimal Transport
  ☆22Updated 8 years ago
• maremun / swg
  Sliced Wasserstein Generator
  ☆23Updated 6 years ago
• joeybose / HyperbolicNF
  ICML 2020 Paper: Latent Variable Modelling with Hyperbolic Normalizing Flows
  ☆53Updated 2 years ago
• kimiandj / gsw
  ☆39Updated 5 years ago
• alexalemi / vib_demo
  ☆89Updated 3 years ago
• MichaelArbel / Scaled-MMD-GAN
  Scaled MMD GAN
  ☆36Updated 5 years ago
• CUAI / Differentiable-Frechet-Mean
  [ICML 2020] Differentiating through the Fréchet Mean (https://arxiv.org/abs/2003.00335).
  ☆55Updated 3 years ago
• yogeshbalaji / robustOT
  Robust Optimal Transport code
  ☆42Updated 2 years ago
• thibsej / unbalanced-ot-functionals
  It is a repo which allows to compute all divergences derived from the theory of entropically regularized, unbalanced optimal transport. I…
  ☆28Updated 2 years ago
• dimkal89 / rvae
  ☆37Updated 4 years ago
• fengliu90 / DK-for-TST
  This is the source code for Learning Deep Kernels for Non-Parametric Two-Sample Tests (ICML2020).
  ☆48Updated 3 years ago
• 1Konny / WAE-pytorch
  ☆68Updated 5 years ago
• fpcasale / GPPVAE
  Gaussian Process Prior Variational Autoencoder
  ☆83Updated 6 years ago