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Few Shot Generative Model Adaption via Relaxed Spatial Structural Alignment

CVPR 2022 · Label-efficient and robust learning › Domain adaptation and generalization · approach: Cross-domain few-shot with transformers · 69 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use RSSA instead of IDCStyleGANv2 few-shot adaptation · Faces → Sketches, 10-shotSCS: 0.437 -> 0.511Helped
Use RSSA instead of IDCStyleGANv2 few-shot adaptation · Churches → Haunted Houses, 5-shotSCS: 0.424 -> 0.582Helped
Use RSSA instead of the strongest baselineStyleGANv2 few-shot adaptation · Faces → Van Gogh, 10-shotIS: 2.69$^{\pm0.18}$ -> 1.77$^{\pm0.06}$Hurt
Remove spatial structural consistency loss (ablation)RSSA · Faces → Van Gogh, 10-shotSCS: 0.449 -> 0.702Hurt
Add self-correlation consistency loss (ablation)RSSA without spatial structural consistency loss · Faces → Van Gogh, 10-shotSCS: 0.449 -> 0.684Helped
Add disturbance-correlation consistency loss (ablation)RSSA without spatial structural consistency loss · Faces → Van Gogh, 10-shotSCS: 0.449 -> 0.671Helped
Use both structural consistency losses instead of self-correlation loss only (ablation)RSSA · Faces → Van Gogh, 10-shotSCS: 0.684 -> 0.702Helped
Use both structural consistency losses instead of disturbance-correlation loss only (ablation)RSSA · Churches → Haunted Houses, 10-shotSCS: 0.548 -> 0.573Helped
Add latent space compression (ablation)RSSA · Faces → Sketches, 10-shotadaptation speedHelped

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