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Research map / Image generation / Autoregressive and latent generation

Picture That Sketch: Photorealistic Image Generation From Abstract Sketches

CVPR 2023 · Image generation › Autoregressive and latent generation · approach: Classical generative image synthesis · 64 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Replace autoregressive mapping with the baseline latent mapper (ablation)Picture That Sketch · ShoeV2FID: 35.85 -> 57.27Hurt
Replace autoregressive mapping with the baseline latent mapper (ablation)Picture That Sketch · ChairV2FID: 90.21 -> 111.99Hurt
Remove fine-grained FG-SBIR discriminative loss (ablation)Picture That Sketch · ShoeV2FID: 35.85 -> 50.29Hurt
Remove fine-grained FG-SBIR discriminative loss (ablation)Picture That Sketch · ChairV2FID: 90.21 -> 104.29Hurt
Remove the photo-to-photo teacher E_r (ablation)Picture That Sketch · ShoeV2FID: 35.85 -> 46.87Hurt
Remove the photo-to-photo teacher E_r (ablation)Picture That Sketch · ChairV2FID: 90.21 -> 99.38Hurt
Use naive W+ encoding instead of the proposed mapper (ablation)Picture That Sketch · ShoeV2FID: 35.85 -> 46.87Hurt
Use naive W+ encoding instead of the proposed mapper (ablation)Picture That Sketch · ChairV2FID: 90.21 -> 99.24Hurt
Embed the sketch as a single W latent vector (ablation)Picture That Sketch · ShoeV2FID: 35.85 -> 52.35Hurt
Embed the sketch as a single W latent vector (ablation)Picture That Sketch · ChairV2FID: 90.21 -> 107.99Hurt

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