Research map / Image generation / GANs
Banach Wasserstein GAN
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the L^10 norm instead of the L^2 norm (ablation) | BWGAN · CIFAR-10 | Inception score | Helped |
| Use the L^4 norm (ablation) | BWGAN · CIFAR-10 | FID | Helped |
| Use a negative Sobolev order or a larger L^p value (ablation) | BWGAN · CIFAR-10 | image quality (Inception score and FID) | Helped |
| Use Sobolev orders between -1 and 0 (ablation) | BWGAN · CelebA | FID | Helped |
| Use the L^10 norm (ablation) | BWGAN · CelebA | training stability | Hurt |
Closest papers by meaning
- A Two-Step Computation of the Exact GAN Wasserstein Distance (ICML 2018 · similarity 0.94)
- Wasserstein of Wasserstein Loss for Learning Generative Models (ICML 2019 · similarity 0.93)
- Do WGANs succeed because they minimize the Wasserstein Distance? Lessons from Discrete Generators (ICLR 2025 · similarity 0.92)
- Generative Modeling Using the Sliced Wasserstein Distance (CVPR 2018 · similarity 0.92)
- Wasserstein Generative Adversarial Networks (ICML 2017 · similarity 0.91)
- Towards Generalized Implementation of Wasserstein Distance in GANs (AAAI 2021 · similarity 0.91)
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