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EditGAN: High-Precision Semantic Image Editing

NeurIPS 2021 · Image generation › Editing and style transfer · approach: GAN inversion for editing · 3 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use segmentation-guided EditGAN instead of MaskGANSmile editing · Smile edit benchmark (400 images)Attribute accuracy (%): 77.3 -> 91.5Helped
Use segmentation-guided EditGAN instead of InterFaceGANSmile editing · Smile edit benchmark (400 images)ID score: 0.7295 -> 0.7047Hurt
Add 30 self-supervised refinement steps (ablation)EditGAN smile edit · Smile edit benchmark (400 images)ID score: 0.7047 -> 0.7452Helped
Add 30 self-supervised refinement steps (ablation)EditGAN smile edit · Smile edit benchmark (400 images)Attribute accuracy (%): 91.5 -> 85.8Hurt
Add 30 self-supervised refinement steps (ablation)EditGAN smile edit · Smile edit benchmark (400 images)FID: 41.74 -> 40.83Helped
Use Encoding4Editing encoder in LocalEditingLocalEditing · Smile edit benchmark (400 images)Attribute accuracy (%): 26.0 -> 41.75Helped
Use Encoding4Editing encoder in LocalEditingLocalEditing · Smile edit benchmark (400 images)FID: 41.26 -> 48.28Hurt
Increase editing-vector scale (ablation)EditGAN · Smile edit benchmarkFIDHurt
Add self-supervised refinement to editing-vector application (ablation)EditGAN · Smile edit benchmarkFIDHelped
Add more than 30 refinement steps (ablation)EditGAN · Smile edit benchmarkediting performanceNo clear effect

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