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Diversity-Sensitive Conditional Generative Adversarial Networks

ICLR 2019 · Image generation › GANs · approach: Conditional GANs · 150 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add diversity regularization to cGANcGAN image-to-image translation · label→imageLPIPS: 0.01 -> 0.18Helped
Add diversity regularization to cGANcGAN image-to-image translation · label→imageFID: 85.07 -> 57.20Helped
Add diversity regularization to cGANcGAN image-to-image translation · map→imageLPIPS: 0.02 -> 0.13Helped
Add diversity regularization to cGANcGAN image-to-image translation · map→imageFID: 90.08 -> 49.92Helped
Add diversity regularization to cGANcGAN image-to-image translation · edge→photoLPIPS: 0.02 -> 0.12Helped
Add diversity regularization to cGANcGAN image-to-image translation · edge→photoFID: 31.80 -> 23.06Mixed
Replace output-space distance with discriminator feature-space distance (ablation)DSGAN inpainting · CelebA image inpaintingLPIPS: 0.01 -> 0.05Helped
Replace output-space distance with discriminator feature-space distance (ablation)DSGAN inpainting · CelebA image inpaintingFID: 13.95 -> 13.94Helped
Increase regularization weight to λ=20 (ablation)DSGAN label→image · label→imageFID: FID=191Hurt
Increase regularization weight to λ=20 (ablation)DSGAN label→image · label→imageLPIPS: LPIPS=0.20Helped
Add diversity regularization to pix2pixHDpix2pixHD · CityscapesLPIPS: 0.00 -> 0.12Helped
Add diversity regularization to pix2pixHDpix2pixHD · CityscapesSegmentation accuracy (%): 0.93 -> 0.92Hurt

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