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Extremal Domain Translation with Neural Optimal Transport

NeurIPS 2023 · Image generation › Editing and style transfer · approach: Image-to-image translation · 2 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Increase the IT weight from w=1 to w=8 (ablation)IT · Image translation benchmarksℓ² transport cost: 0.297 -> 0.094 (CelebA → anime); 0.368 -> 0.252 (Handbag → shoes); 0.603 -> 0.408 (Textures → chairs); 0.224 -> 0.196 (FHelped
Increase the IT weight from w=1 to w=8 (ablation)IT · Image translation benchmarksFID: 14.65 -> 22.84 (CelebA → anime); 27.10 -> 53.80 (Handbag → shoes); 20.95 -> 23.67 (FFHQ → comics)Mixed
Add a small identity loss (λ=50) (ablation)CycleGAN · CelebA → animeFID: 22.9 -> 20.8Helped
Increase CycleGAN identity-loss weight from λ=0 to λ=500 (ablation)CycleGAN · CelebA → animeℓ² transport cost: 0.33 -> 0.01Helped
Increase CycleGAN identity-loss weight from λ=0 to λ=500 (ablation)CycleGAN · CelebA → animeFID: 22.9 -> 122.8Hurt
Add ℓ² identity loss to StarGAN-v2 (λ=10) (ablation)StarGAN-v2 · Handbag → shoesℓ² transport cost: 0.739 -> 0.244Mixed
Increase StarGAN-v2 similarity-loss weight from λ=0 to λ=10 (ablation)StarGAN-v2 · Handbag → shoesFID: 25.45 -> 22.36Helped
Use an ℓ² similarity term in CycleGAN instead of the paper's ℓ¹ identity loss (ablation)CycleGAN · Image translationℓ² transport costHurt
Use a soft unbalanced-OT constraint instead of hard partial-OT/IT constraints (ablation)Unbalanced OT · Wi-Fi toy exampletarget-mass alignmentHurt
Use GAN+ℓ² or unbalanced OT for the Accept toy task (ablation)GAN+ℓ² and unbalanced OT · Accept toy exampletarget-domain translationHurt

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