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Found 10 papers, 3 papers with code
Efficient Solvers for Partial Gromov-Wasserstein
The partial Gromov-Wasserstein (PGW) problem facilitates the comparison of measures with unequal masses residing in potentially distinct metric spaces, thereby enabling unbalanced and partial matching across these spaces.
Stereographic Spherical Sliced Wasserstein Distances
Comparing spherical probability distributions is of great interest in various fields, including geology, medical domains, computer vision, and deep representation learning.
LCOT: Linear circular optimal transport
The optimal transport problem for measures supported on non-Euclidean spaces has recently gained ample interest in diverse applications involving representation learning.
Partial Transport for Point-Cloud Registration
In this paper, we approach the point-cloud registration problem through the lens of optimal transport theory and first propose a comprehensive set of non-rigid registration methods based on the optimal partial transportation problem.
PT$\mathrm{L}^{p}$: Partial Transport $\mathrm{L}^{p}$ Distances
In this paper, we introduce partial transport $\mathrm{L}^{p}$ distances as a new family of metrics for comparing generic signals, benefiting from the robustness of partial transport distances.
Linear Optimal Partial Transport Embedding
To address these limitations, variants of the OT problem, including unbalanced OT, Optimal partial transport (OPT), and Hellinger Kantorovich (HK), have been proposed.
Sliced Optimal Partial Transport
Optimal transport (OT) has become exceedingly popular in machine learning, data science, and computer vision.
Wasserstein Task Embedding for Measuring Task Similarities
Lastly, we leverage the 2-Wasserstein embedding framework to embed tasks into a vector space in which the Euclidean distance between the embedded points approximates the proposed 2-Wasserstein distance between tasks.
Understanding Entropic Regularization in GANs
We show that the optimal generator can be learned to accuracy $\epsilon$ with $O(1/\epsilon^2)$ samples from the target distribution.
Information Constrained Optimal Transport: From Talagrand, to Marton, to Cover
Following Marton's approach, we show that the new transportation cost inequality can be used to recover old and new concentration of measure results.
