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Research map / Image generation / Editing and style transfer

High-Fidelity GAN Inversion for Image Attribute Editing

CVPR 2022 · Image generation › Editing and style transfer · approach: GAN inversion for editing · 237 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add distortion consultation to the low-rate e4e encodere4e inversion on CelebA-HQ · CelebA-HQ first 1,500 imagesMAE: .0919±.0008 -> .0617±.0004Helped
Add distortion consultation to the low-rate e4e encodere4e inversion on CelebA-HQ · CelebA-HQ first 1,500 imagesSSIM: .742±.007 -> .877±.002Helped
Add distortion consultation to the low-rate e4e encodere4e inversion on CelebA-HQ · CelebA-HQ first 1,500 imagesLPIPS: .221±.003 -> .127±.001Helped
Add distortion consultation to the low-rate e4e encodere4e inversion on CelebA-HQ · CelebA-HQ first 1,500 imagesinference time: 0.11s -> 0.24sHurt
Add distortion consultation to the low-rate encoder (ablation)GAN inversion and editing · Face and car domainsimage-specific detail preservationHelped
Add adaptive distortion alignment (ADA) before consulting distortion during editing (ablation)DCI attribute editing · Face and car image editinggenerated image qualityHelped
Replace feature-space alignment and fusion with image-space distortion addition without warping (ablation)DCI attribute editing · Image editingartifact-free image qualityHurt
Use landmark-based warping and image-space fusion instead of ADA and consultation fusion (ablation)DCI attribute editing · Image editingartifact-free image qualityHurt
Apply ADA to large distortion-map misalignments beyond those covered by training augmentationDCI attribute editing · Editing with large viewpoint changesalignment qualityHurt

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