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Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Coulomb GAN instead of WGAN-GP with TTUR | GAN image generation · CelebA | FID: 9.3 vs ~4.2 | Hurt |
| Use Coulomb GAN instead of WGAN-GP with TTUR | GAN image generation · LSUN | FID: 31.2 vs ~9.5 | Hurt |
| Use Coulomb GAN instead of WGAN-GP with TTUR | GAN image generation · CIFAR-10 | FID: 27.3 vs 24.8 | Hurt |
| Use Coulomb GAN instead of WGAN-GP with TTUR | Google Billion Word text generation · Google Billion Word, 4 grams | Normalized Jensen-Shannon divergence: 0.35 vs 0.35 | No clear effect |
| Use Coulomb GAN instead of WGAN-GP with TTUR | Google Billion Word text generation · Google Billion Word, 6 grams | Normalized Jensen-Shannon divergence: 0.74 vs 0.74 | No clear effect |
| Use a low-dimensional Plummer kernel instead of a Gaussian kernel | Coulomb GAN / MMD-GAN comparison · Image datasets | FID: CelebA: 63.2 vs 9.3; LSUN: 94.9 vs 31.2; CIFAR10: 38.2 vs 27.3 | Mixed |
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- Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step (ICLR 2018 · similarity 0.90)
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