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UCTGAN: Diverse Image Inpainting Based on Unsupervised Cross-Space Translation

CVPR 2020 · Image generation › Autoregressive and latent generation · approach: Classical generative image synthesis · 208 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add cross semantic attention (ablation)UCTGAN · CelebA-HQ test, center maskPSNR: 26.0223 -> 26.3833Helped
add cross semantic attention (ablation)UCTGAN · CelebA-HQ test, center maskSSIM: 0.8732 -> 0.8862Helped
add cross semantic attention (ablation)UCTGAN · CelebA-HQ test, center maskIS: 3.0011 -> 3.0127Helped
add cross semantic attention (ablation)UCTGAN · CelebA-HQ test, center maskMIS: 0.0174 -> 0.0178Helped
add cross semantic attention (ablation)UCTGAN · CelebA-HQ test, center maskl1 loss (%): 1.65 -> 1.51Helped
use instance-image guidance instead of standard normal noise (ablation)UCTGAN · CelebA-HQ test, center maskMIS: 0.0174 -> 0.0178Helped
use instance-image guidance instead of standard normal noise (ablation)UCTGAN · CelebA-HQ test, center maskPSNR: 25.9700 -> 26.3833Helped
add low-dimension loss Llrec (ablation)UCTGAN · CelebA-HQ, 1K center-masked imagesLPIPS(Iout): 0.017 -> 0.030Helped
add low-dimension loss Llrec (ablation)UCTGAN · CelebA-HQ, 1K center-masked imagesLPIPS(Iout(m)): 0.032 -> 0.092Helped
use instance-image guidance instead of standard normal noise (ablation)UCTGAN · CelebA-HQ, 1K center-masked imagesLPIPS(Iout(m)): 0.062 -> 0.092Helped

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