Alternatives and similar repositories for COOT

Users that are interested in COOT are comparing it to the libraries listed below

• thibsej / unbalanced_gromov_wasserstein
  Implementation of the Gromov-Wasserstein distance to the setting of Unbalanced Optimal Transport
  ☆45Updated 2 years ago
• AmirTag / OT-ICNN
  Learning the optimal transport map via input convex neural neworks
  ☆42Updated 5 years ago
• trneedham / Spectral-Gromov-Wasserstein
  Graph matching and clustering by comparing heat kernels via optimal transport.
  ☆27Updated 3 years ago
• ratschlab / SVGP-VAE
  Tensorflow implementation for the SVGP-VAE model.
  ☆22Updated 4 years ago
• KrishnaswamyLab / DiffusionEMD
  Code corresponding to the paper Diffusion Earth Mover's Distance and Distribution Embeddings
  ☆38Updated last year
• vivienseguy / Large-Scale-OT
  Stochastic algorithms for computing Regularized Optimal Transport
  ☆58Updated 7 years ago
• tvayer / SGW
  Code for Sliced Gromov-Wasserstein
  ☆69Updated 6 years ago
• samirchowdhury / HyperCOT
  Code for computing Hypergraph Co-Optimal Transport distances
  ☆17Updated 2 years ago
• geomstats / challenge-iclr-2021
  GitHub repository for the ICLR Computational Geometry & Topology Challenge 2021
  ☆53Updated 3 years ago
• iamalexkorotin / Wasserstein2Benchmark
  A set of tests for evaluating large-scale algorithms for Wasserstein-2 transport maps computation (NeurIPS 2021)
  ☆43Updated 3 years ago
• cscarv / riemannian-metric-learning-ot
  ☆18Updated 2 years ago
• justkolesov / Wasserstein1Benchmark
  A set of tests for evaluating large-scale algorithms for Wasserstein-1 transport computation (NeurIPS'22).
  ☆21Updated last year
• navid-naderi / WEGL
  The implementation code for our paper Wasserstein Embedding for Graph Learning (ICLR 2021).
  ☆35Updated 4 years ago
• kasparmartens / NeuralDecomposition
  PyTorch implementation of the paper "Neural Decomposition: Functional ANOVA with Variational Autoencoders"
  ☆25Updated 5 years ago
• francoispierrepaty / SubspaceRobustWasserstein
  Source code for the ICML2019 paper "Subspace Robust Wasserstein Distances"
  ☆29Updated 6 years ago
• tvayer / FGW
  Code for Optimal Transport for structured data with application on graphs
  ☆103Updated 3 years ago
• iamalexkorotin / Wasserstein2GenerativeNetworks
  PyTorch implementation of "Wasserstein-2 Generative Networks" (ICLR 2021)
  ☆57Updated 2 years ago
• facebookresearch / w2ot
  Euclidean Wasserstein-2 optimal transportation
  ☆47Updated 2 years ago
• BorgwardtLab / topological-autoencoders
  Code for the paper "Topological Autoencoders" by Michael Moor, Max Horn, Bastian Rieck, and Karsten Borgwardt.
  ☆153Updated 3 years ago
• trneedham / gromov-wasserstein-statistics
  Statistics on the space of asymmetric networks via Gromov-Wasserstein distance
  ☆15Updated 5 years ago
• IlyaTrofimov / MTopDiv
  [NeurIPS 2021] Manifold Topology Divergence: a Framework for Comparing Data Manifolds
  ☆15Updated 3 years ago
• mblondel / smooth-ot
  Python implementation of smooth optimal transport.
  ☆61Updated 4 years ago
• CUAI / Differentiable-Frechet-Mean
  [ICML 2020] Differentiating through the Fréchet Mean (https://arxiv.org/abs/2003.00335).
  ☆59Updated 4 years ago
• hchau630 / LSC
  Code for "Disentangling images with Lie group transformations and sparse coding" (2023).
  ☆13Updated 4 years ago
• jeanfeydy / global-divergences
  MMD, Hausdorff and Sinkhorn divergences scaled up to 1,000,000 samples.
  ☆57Updated 6 years ago
• siplab-gt / VAELLS
  Code for "Variational Autoencoder with Learned Latent Structure"
  ☆34Updated 4 years ago
• ParthaEth / Regularized_autoencoders-RAE-
  ☆125Updated 2 years ago
• tlacombe / topologicalUncertainty
  ☆11Updated 3 years ago
• claying / OTK
  A Pytorch implementation of the optimal transport kernel embedding
  ☆118Updated 4 years ago
• tychovdo / lila
  Code for "Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations"
  ☆23Updated 3 years ago