Research map / Learning theory and algorithms / Optimal transport
Building the Bridge of Schrödinger: A Continuous Entropic Optimal Transport Benchmark
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use EOT solvers LSOT and SCONES at medium/large epsilon | LSOT, SCONES · Mixtures pairs, epsilon 1 and 10 | cBW2-UVP | Hurt |
| run LSOT and SCONES at epsilon 0.1 | LSOT, SCONES · Mixtures pairs, epsilon 0.1 | BW2-UVP | Hurt |
| use conditional normalizing flow as generator in NOT | NOT · Mixtures pairs | cBW2-UVP | Helped |
| increase epsilon from 0.1 to 10 | SB solvers (ENOT, MLE-SB, DiffSB, FB-SDE) · Mixtures pairs | BW2-UVP | Hurt |
| use default hyperparameters at epsilon 10 | DiffSB, FB-SDE-A, FB-SDE-J · Mixtures pairs, epsilon 10 | BW2-UVP | Hurt |
| increase epsilon from 0.1 to 10 | ENOT · CelebA 64x64 images pairs | image restoration precision | Mixed |
| apply SCONES, DiffSB, FB-SDE-A, NOT to high-dimensional images | SCONES, DiffSB, FB-SDE-A, NOT · CelebA 64x64 images pairs | training convergence | Hurt |
| EgNOT instead of other EOT solvers for matching target marginal | EgNOT · Mixtures pairs | BW2-UVP | Helped |
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