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Research map / Image generation / Editing and style transfer

PetsGAN: Rethinking Priors for Single Image Generation

AAAI 2022 · Image generation › Editing and style transfer · approach: Texture and sketch synthesis · 22 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use joint rather than cascaded training (ablation)PetsGAN · Places50, LSUN50, ImageNet50SIFID: 0.10 -> 0.08; 0.11 -> 0.09; 0.50 -> 0.21Helped
Use joint rather than cascaded training (ablation)PetsGAN · Places50, LSUN50, ImageNet50LPIPS: 0.31 -> 0.31; 0.34 -> 0.33; 0.37 -> 0.35Mixed
Add patch adversarial loss (ablation)PetsGAN · Places50, LSUN50, ImageNet50SIFID: 0.23 -> 0.08; 0.23 -> 0.09; 0.30 -> 0.21Helped
Add patch adversarial loss (ablation)PetsGAN · Places50, LSUN50, ImageNet50LPIPS: 0.33 -> 0.31; 0.38 -> 0.33; 0.39 -> 0.35Mixed
Add external prior (ablation)PetsGAN · Places50, LSUN50, ImageNet50LPIPS: 0.18 -> 0.31; 0.21 -> 0.33; 0.22 -> 0.35Helped
Add external prior (ablation)PetsGAN · Places50, LSUN50, ImageNet50SIFID: 0.11 -> 0.08; 0.11 -> 0.09; 0.25 -> 0.21Helped
Add internal prior (ablation)PetsGAN · Places50, LSUN50, ImageNet50SIFID: 0.20 -> 0.08; 0.19 -> 0.09; 0.40 -> 0.21Helped
Add internal prior (ablation)PetsGAN · Places50, LSUN50, ImageNet50LPIPS: 0.23 -> 0.31; 0.26 -> 0.33; 0.33 -> 0.35Helped

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