A collection of AWESOME things about domian adaptation
POT : Python Optimal Transport
TorchCFM: a Conditional Flow Matching library
An easy-to-use Python library for processing and manipulating 3D point clouds and meshes.
Optimal transport tools implemented with the JAX framework, to solve large scale matching problems of any flavor.
Approximating Wasserstein distances with PyTorch
[NeurIPS 2024] GaussianCube: A Structured and Explicit Radiance Representation for 3D Generative Modeling
FoldFlow: SE(3)-Stochastic Flow Matching for Protein Backbone Generation
PyTorch implementation of "Neural Optimal Transport" (ICLR 2023 Spotlight)
PyTorch for Quantitative Finance : Payoffs are Activations
[ICLR2023] PLOT: Prompt Learning with Optimal Transport for Vision-Language Models
Multi-omic single-cell optimal transport tools
A software package for analyzing snapshots of developmental processes
Neural Architecture Search with Bayesian Optimisation and Optimal Transport
Morphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathology - CVPR 2024
Play, learn, solve, and analyze No-Limit Texas Hold Em. Implementation follows from Monte Carlo counter-factual regret minimization over with hierarchical K-means imperfect recall abstractions.
TorchDR - PyTorch Dimensionality Reduction
CVPR 2020, Semantic Correspondence as an Optimal Transport Problem, Pytorch Implementation.
Implementation of the Sliced Wasserstein Autoencoder using PyTorch
Optimal transport algorithms for Julia
Add a description, image, and links to the optimal-transport topic page so that developers can more easily learn about it.
To associate your repository with the optimal-transport topic, visit your repo's landing page and select "manage topics."