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Learning to Predict Layout-to-image Conditional Convolutions for Semantic Image Synthesis

NeurIPS 2019 · Image generation › GANs · approach: Conditional GANs · 91 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use CC-FPSE instead of SPADESemantic image synthesis · COCO-StuffmIOU: 37.4 -> 41.6Helped
Use CC-FPSE instead of SPADESemantic image synthesis · COCO-StuffAccu: 67.9 -> 70.7Helped
Use CC-FPSE instead of SPADESemantic image synthesis · COCO-StuffFID: 22.6 -> 19.2Helped
Use CC-FPSE instead of SPADESemantic image synthesis · CityscapesmIOU: 62.3 -> 65.5Helped
Use CC-FPSE instead of SPADESemantic image synthesis · CityscapesAccu: 81.9 -> 82.3Helped
Use CC-FPSE instead of SIMSSemantic image synthesis · CityscapesFID: 49.7 -> 54.3Hurt
Use CC-FPSE instead of SPADESemantic image synthesis · ADE20KmIOU: 38.5 -> 43.7Helped
Use CC-FPSE instead of SPADESemantic image synthesis · ADE20KAccu: 79.9 -> 82.9Helped
Use CC-FPSE instead of SPADESemantic image synthesis · ADE20KFID: 33.9 -> 31.7Helped
Replace SPADE generator with conditional convolution generator (ablation)SPADE baseline with MsPatch discriminator · COCO-StuffmIOU: 35.2 -> 36.2Helped
Add feature pyramid structure to the CC weight prediction network (ablation)CC generator with MsPatch discriminator · COCO-StuffmIOU: 36.2 -> 36.7Helped
Add semantics embedding to the discriminator (ablation)CC generator with feature pyramid weight prediction · COCO-StuffmIOU: 36.7 -> 40.4Helped

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