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Found 3 papers, 1 papers with code
Unbalancedness in Neural Monge Maps Improves Unpaired Domain Translation
We hence establish UOT-FM as a principled method for unpaired image translation.
Entropic (Gromov) Wasserstein Flow Matching with GENOT
Optimal transport (OT) theory has reshaped the field of generative modeling: Combined with neural networks, recent \textit{Neural OT} (N-OT) solvers use OT as an inductive bias, to focus on ``thrifty'' mappings that minimize average displacement costs.
The Monge Gap: A Regularizer to Learn All Transport Maps
That gap quantifies how far a map $T$ deviates from the ideal properties we expect from a $c$-OT map.
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