Research map / Image generation / GANs
Quality Aware Generative Adversarial Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add SSIM-based quality aware distance penalty | WGAN-GP · CIFAR-10 | FID: 40.2 -> 13.91 | Helped |
| add NIQE-based gradient penalty | WGAN-GP · CIFAR-10 | FID: 40.2 -> 12.4697 | Helped |
| add SSIM-based quality aware distance penalty | WGAN-GP · CIFAR-10 | IS: 7.86 -> 8.37 | Helped |
| add NIQE-based gradient penalty | WGAN-GP · CIFAR-10 | IS: 7.86 -> 7.87 | No clear effect |
| add SSIM-based quality aware distance penalty | WGAN-GP · STL-10 48x48 | FID: 55.1 -> 19.77 | Helped |
| add NIQE-based gradient penalty | WGAN-GP · CelebA 64x64 | FID: 12.89 -> 6.504 | Helped |
| add SSIM-based quality aware distance penalty | WGAN-GP · CelebA 64x64 | FID: 12.89 -> 6.421 | Helped |
| add SSIM-based regularizer to PGGAN | PGGAN · CelebA 256x256 | FID: 62.86 -> 40.83 | Helped |
| add NIQE-based regularizer to PGGAN | PGGAN · CelebA 128x128 | FID: 64.50 -> 49.80 | Helped |
Closest papers by meaning
- Improved Consistency Regularization for GANs (AAAI 2021 · similarity 0.88)
- Progressive Growing of GANs for Improved Quality, Stability, and Variation (ICLR 2018 · similarity 0.88)
- NICE: NoIse-modulated Consistency rEgularization for Data-Efficient GANs (NeurIPS 2023 · similarity 0.88)
- Geometry Score: A Method For Comparing Generative Adversarial Networks (ICML 2018 · similarity 0.88)
- Analyzing and Improving the Image Quality of StyleGAN (CVPR 2020 · similarity 0.88)
- SMaRt: Improving GANs with Score Matching Regularity (ICML 2024 · similarity 0.88)
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