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High-Resolution GAN Inversion for Degraded Images in Large Diverse Datasets

AAAI 2023 · Image generation › Autoregressive and latent generation · approach: Generative latent space tooling · 5 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use CRI instead of PTIStyleGAN-XL inversion · ImageNet val inpaintingLPIPS: 0.1219 -> 0.1164Helped
use CRI instead of PTIStyleGAN-XL inversion · ImageNet val colorizationFID: 47.51 -> 45.90Helped
use CRI instead of PTIStyleGAN-XL inversion · ImageNet val SRNIQE: 5.5659 -> 5.3065Helped
use CRI instead of the strongest baselineStyleGAN2 inversion · CelebA-HQ inpaintingFID: 53.48 -> 48.00Helped
use CRI instead of the strongest baselineStyleGAN2 inversion · CelebA-HQ colorizationLPIPS: 0.1621 -> 0.1505Helped
use CRI instead of the strongest baselineStyleGAN2 inversion · CelebA-HQ SRFID: 60.76 -> 58.88Helped
use CRI with StyleGAN-XL instead of DGP with BigGANImageNet restoration · ImageNet colorizationFID: 189.42 -> 92.48Helped
add L2 regularized offset rather than no regularization (ablation)StyleGAN2 CRI · CelebA-HQ colorizationLPIPS: 0.1996 -> 0.1560Helped
add L2 regularized offset rather than no regularization (ablation)StyleGAN2 CRI · CelebA-HQ colorizationID Similarity: 0.7251 -> 0.5756Hurt
replace L2 offset regularization with L1 (ablation)StyleGAN2 CRI · CelebA-HQ colorizationLPIPS: 0.1560 -> 0.1694Hurt
replace L2 offset regularization with L1 (ablation)StyleGAN2 CRI · CelebA-HQ colorizationID Similarity: 0.5756 -> 0.1955Hurt
increase the number of clustering centroids (ablation)CRI · ImageNet colorizationqualitative performanceHelped

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