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Research map / Learning theory and algorithms / Optimal transport

Mapping Estimation for Discrete Optimal Transport

NeurIPS 2016 · Learning theory and algorithms › Optimal transport · approach: Transport map estimation · 60 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
jointly learn coupling and linear map (OTLin) instead of classic OTOT domain adaptation · Office-Caltech meanaccuracy (%): 68.6 -> 84.1Helped
jointly learn coupling and linear map (OTLin) instead of entropic OTEOT domain adaptation · Office-Caltech meanaccuracy (%): 74.6 -> 84.1Helped
linear map instead of kernel map (ablation)OTLin vs OTKer · Office-Caltech meanaccuracy (%): 79.6 -> 84.1Helped
linear map instead of kernel map (ablation)OTLin vs OTKer · Moons 90 degreesaccuracy (%): 56.3 -> 67.9Helped
add bias term to map (OTLinB vs OTLin) (ablation)OTLin · Office-Caltech meanaccuracy (%): 84.1 -> 84.1No clear effect
add bias term to kernel map (OTKerB vs OTKer) (ablation)OTKer · Office-Caltech meanaccuracy (%): 79.6 -> 80.0Helped
evaluate learned map T instead of coupling gamma (ablation)OTLin · Office-Caltech meanaccuracy (%): 84.1 -> 84.1No clear effect
proposed joint learning versus classic OT at large rotationOTLin · Moons 90 degreesaccuracy (%): 51.0 -> 67.9Helped
proposed OTLin versus unadapted 1NNOTLin · Moons 90 degreesaccuracy (%): 19.4 -> 67.9Helped
OT-based methods at small rotationOT variants · Moons 10-40 degreesaccuracy (%): 100.0No clear effect
proposed OTLin versus OTE/L1L2 on harder rotationOTLin · Moons 70 degreesaccuracy (%): 89.0 -> 88.0Mixed
kernel map vs linear map on individual Office-Caltech tasks (ablation)OTKer · A->Caccuracy (%): 81.8 -> 84.4Helped

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