min-GSGW learns coupled nonlinear slicers to produce a rigid-motion-invariant, scalable approximation to the Gromov-Wasserstein distance and its transport plans.
Scalable gromov-wasserstein learning for graph partitioning and matching
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Min Generalized Sliced Gromov Wasserstein: A Scalable Path to Gromov Wasserstein
min-GSGW learns coupled nonlinear slicers to produce a rigid-motion-invariant, scalable approximation to the Gromov-Wasserstein distance and its transport plans.