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Found 7 papers, 4 papers with code
Unbalanced Optimal Transport meets Sliced-Wasserstein
In parallel, unbalanced OT was designed to allow comparisons of more general positive measures, while being more robust to outliers.
Unbalanced Optimal Transport, from Theory to Numerics
Optimal Transport (OT) has recently emerged as a central tool in data sciences to compare in a geometrically faithful way point clouds and more generally probability distributions.
Faster Unbalanced Optimal Transport: Translation invariant Sinkhorn and 1-D Frank-Wolfe
In this work, we identify the cause for this deficiency, namely the lack of a global normalization of the iterates, which equivalently corresponds to a translation of the dual OT potentials.
Unbalanced minibatch Optimal Transport; applications to Domain Adaptation
Optimal transport distances have found many applications in machine learning for their capacity to compare non-parametric probability distributions.
The Unbalanced Gromov Wasserstein Distance: Conic Formulation and Relaxation
The GW distance is however limited to the comparison of metric measure spaces endowed with a probability distribution.
Sinkhorn Divergences for Unbalanced Optimal Transport
Optimal transport induces the Earth Mover's (Wasserstein) distance between probability distributions, a geometric divergence that is relevant to a wide range of problems.
Interpolating between Optimal Transport and MMD using Sinkhorn Divergences
Comparing probability distributions is a fundamental problem in data sciences.
Statistics Theory Statistics Theory 62
