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Spatial Fusion GAN for Image Synthesis

CVPR 2019 · Image generation › GANs · approach: Large-scale GAN latent analysis · 167 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Combine geometry and appearance synthesis instead of random alignment and appearance (ablation)SF-GAN · Average across six text datasetsrecognition accuracy: 47.8 -> 53.7Helped
Add the geometry synthesizer to the random-alignment baseline (ablation)SF-GAN · Average across six text datasetsrecognition accuracy: 47.8 -> 49.5Helped
Add the appearance synthesizer to the random-alignment baseline (ablation)SF-GAN · Average across six text datasetsrecognition accuracy: 47.8 -> 50.1Helped
Add appearance synthesis to geometry-synthesizer output (ablation)SF-GAN · Average across six text datasetsrecognition accuracy: 49.5 -> 53.7Helped
Add geometry synthesis to appearance synthesis with random alignments (ablation)SF-GAN · Average across six text datasetsrecognition accuracy: 50.1 -> 53.7Helped
Use SF-GAN-synthesized rather than Zhan-synthesized training imagesScene text recognition training · Average across six text datasetsrecognition accuracy: 51.1 -> 53.7Helped
Use SF-GAN rather than ST-GAN to synthesize portrait accessoriesPortrait wearing · Glass wearingclassified as real (%): 41.7 -> 62.0Helped

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