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Manifold Learning Benefits GANs

CVPR 2022 · Image generation › GANs · approach: Generative adversarial network theory · 18 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add LCSA to OmniGANOmniGAN · CIFAR-10, d'=512tFID: 6.88 -> 3.36Helped
Add LCSA to OmniGANOmniGAN · CIFAR-100, d'=512tFID: 9.13 -> 5.22Helped
Add LCSA to OmniGANOmniGAN · ImageNet 64×64IS: 70.59 -> 77.45Helped
Add LCSA to OmniGANOmniGAN · ImageNet 128×128tFID: 8.30 -> 4.83Helped
Add LCSA to StyleGAN2StyleGAN2 · FFHQ 256×256, 140kFID: 3.71 -> 3.32Helped
Add proximity loss (ablation)OmniGAN+LCSA · CIFAR-10, d'=512tFID: 5.15 -> 3.29Helped
Add proximity loss (ablation)OmniGAN+LCSA · CIFAR-100, d'=512tFID: 8.12 -> 5.12Helped
Add adaptive mixing input (ablation)OmniGAN+LCSA · CIFAR-10, d'=512tFID: 4.77 -> 3.29Helped
Add adaptive mixing input (ablation)OmniGAN+LCSA · CIFAR-100, d'=512tFID: 5.54 -> 5.12Helped
Use learned dictionary updates instead of EMA (ablation)OmniGAN+LCSA · CIFAR-100, d'=512tFID: 5.63 -> 5.12Helped
Use adaptive rather than fixed β and γ (ablation)OmniGAN+LCSA · CIFAR-10, d'=512tFID: 4.06 -> 3.29Helped
Use adaptive rather than fixed β and γ (ablation)OmniGAN+LCSA · CIFAR-100, d'=512tFID: 5.46 -> 5.12Helped

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