Official code for "A Normalized Gaussian Wasserstein Distance for Tiny Object Detection"
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Updated
Jun 21, 2022 - Python
Official code for "A Normalized Gaussian Wasserstein Distance for Tiny Object Detection"
MS-Mapping: An Uncertainty-Aware Large-Scale Multi-Session LiDAR Mapping System
Optimal transport algorithms for Julia
PyTorch implementation of slicing adversarial network (SAN)
Measure the distance between two spectra/signals using optimal transport and related metrics
The Wasserstein Distance and Optimal Transport Map of Gaussian Processes
A materials discovery algorithm geared towards exploring high-performance candidates in new chemical spaces.
Source code for the ICML2019 paper "Subspace Robust Wasserstein Distances"
Functional Optimal Transport: Map Estimation and Domain Adaptation for Functional data
1D Wasserstein Statistical Loss in Pytorch
A thorough review of the paper "Learning Embeddings into Entropic Wasserstein Spaces" by Frogner et al. Includes a reproduction of the results on word embeddings.
Multi-Source Domain Adaptation via Optimal Transport for Student-Teacher Learning - UAI 2021
Discovering Conservation Laws using Optimal Transport and Manifold Learning
LAMDA: Label Matching Deep Domain Adaptation - ICML 2021
Unsupervised Domain Adaptation for Acoustic Scene Classification with Wasserstein Distance
Persistence Diagrams in Julia
Header only C++ implementation of the Wasserstein distance (or earth mover's distance)
This Repository Contains Solution to the Assignments of the Generative Adversarial Networks (GANs) Specialization from deeplearning.ai on Coursera Taught by Sharon Zhou, Eda Zhou, Eric Zelikman
My version of cWGAN-gp. Simply my cDCGAN-based but using the Wasserstein Loss and gradient penalty.
Fast Topological Clustering with Wasserstein Distance (ICLR 2022)
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