Earth mover's distances on discrete surfaces
We introduce a novel method for computing the earth mover's distance (EMD) between
probability distributions on a discrete surface. Rather than using a large linear program with …
probability distributions on a discrete surface. Rather than using a large linear program with …
A sliced wasserstein loss for neural texture synthesis
E Heitz, K Vanhoey, T Chambon… - Proceedings of the …, 2021 - openaccess.thecvf.com
We address the problem of computing a textural loss based on the statistics extracted from
the feature activations of a convolutional neural network optimized for object recognition (eg …
the feature activations of a convolutional neural network optimized for object recognition (eg …
On parameter estimation with the Wasserstein distance
Statistical inference can be performed by minimizing, over the parameter space, the
Wasserstein distance between model distributions and the empirical distribution of the data …
Wasserstein distance between model distributions and the empirical distribution of the data …
Projection robust Wasserstein distance and Riemannian optimization
Projection robust Wasserstein (PRW) distance, or Wasserstein projection pursuit (WPP), is a
robust variant of the Wasserstein distance. Recent work suggests that this quantity is more …
robust variant of the Wasserstein distance. Recent work suggests that this quantity is more …
Fréchet means and Procrustes analysis in Wasserstein space
Y Zemel, VM Panaretos - 2019 - projecteuclid.org
Frechet means and Procrustes analysis in Wasserstein space Page 1 Bernoulli 25(2), 2019,
932–976 https://doi.org/10.3150/17-BEJ1009 Fréchet means and Procrustes analysis in …
932–976 https://doi.org/10.3150/17-BEJ1009 Fréchet means and Procrustes analysis in …
A riemannian block coordinate descent method for computing the projection robust wasserstein distance
The Wasserstein distance has become increasingly important in machine learning and deep
learning. Despite its popularity, the Wasserstein distance is hard to approximate because of …
learning. Despite its popularity, the Wasserstein distance is hard to approximate because of …
Recent advances in optimal transport for machine learning
Recently, Optimal Transport has been proposed as a probabilistic framework in Machine
Learning for comparing and manipulating probability distributions. This is rooted in its rich …
Learning for comparing and manipulating probability distributions. This is rooted in its rich …
On projection robust optimal transport: Sample complexity and model misspecification
Optimal transport (OT) distances are increasingly used as loss functions for statistical
inference, notably in the learning of generative models or supervised learning. Yet, the …
inference, notably in the learning of generative models or supervised learning. Yet, the …
Sliced gromov-wasserstein
Recently used in various machine learning contexts, the Gromov-Wasserstein distance (GW)
allows for comparing distributions whose supports do not necessarily lie in the same metric …
allows for comparing distributions whose supports do not necessarily lie in the same metric …
Fast approximation of the sliced-Wasserstein distance using concentration of random projections
Abstract The Sliced-Wasserstein distance (SW) is being increasingly used in machine
learning applications as an alternative to the Wasserstein distance and offers significant …
learning applications as an alternative to the Wasserstein distance and offers significant …