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Photographic Text-to-Image Synthesis With a Hierarchically-Nested Adversarial Network

CVPR 2018 · Image generation › Text-to-image diffusion · approach: Personalized and region-controlled generation · 311 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use hierarchically-nested discriminators at 64, 128, and 256 scales (ablation)HDGAN · CUBInception score: 3.52{\pm}.04 -> 4.15{\pm}.05Helped
add the 64-scale discriminator to the 128- and 256-scale discriminators (ablation)HDGAN · CUBInception score: 3.99{\pm}.04 -> 4.15{\pm}.05Helped
add the 128-scale discriminator to the 64- and 256-scale discriminators (ablation)HDGAN · CUBInception score: 4.14{\pm}.03 -> 4.15{\pm}.05No clear effect
add the 128-scale discriminator to the 64- and 256-scale discriminators (ablation)HDGAN · COCOInception score: 11.29{\pm}.18 -> 11.86{\pm}.18Helped
add the local image loss (ablation)HDGAN · Oxford-102Inception score: 3.12{\pm}.02 -> 3.45{\pm}.07Helped
add the local image loss (ablation)HDGAN · Oxford-102VS similarity: .263{\pm}.130 -> .296{\pm}.130Helped
share the top layers of the hierarchical discriminators instead of using independent discriminators (ablation)HDGAN · text-to-image synthesisperformanceNo clear effect
use instance normalization or layer normalization instead of the reported normalization (ablation)HDGAN · text-to-image synthesisperformanceHurt
use hierarchical-nested adversarial training rather than StackGAN's intermediate text conditioningHDGAN · CUBInception score: 3.7 -> 3.45Helped

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