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Improving GANs Using Optimal Transport
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use optimal transport matching within mini-batch energy distance (vs random matching) (ablation) | OT-GAN on CIFAR-10 · CIFAR-10 | Inception score: 8.47 vs 4.64 | Helped |
| learn transport cost adversarially in critic feature space (vs fixed cosine on pixels) (ablation) | OT-GAN on CIFAR-10 · CIFAR-10 | Inception score: 8.47 vs 4.93 | Helped |
| increase mini-batch size (200 to 8000) (ablation) | OT-GAN on CIFAR-10 · CIFAR-10 | Inception score | Helped |
| OT-GAN objective with mini-batch energy distance vs DAN-S loss under fixed discriminator (consistency test) | GAN on 8-Gaussian mixture · 8 Gaussians toy | modes covered after fixing critic: covers all 8 modes vs collapse to 3 | Helped |
| OT-GAN instead of DCGAN variant on ImageNet dogs | OT-GAN · ImageNet Dogs 128x128 | Inception score: 8.97 vs 8.19 | Helped |
| update generator more often than critic (3:1) | OT-GAN on CIFAR-10 · CIFAR-10 | qualitative stability | No clear effect |
Closest papers by meaning
- Parallel Optimal Transport GAN (CVPR 2019 · similarity 0.93)
- Analyzing and Improving Optimal-Transport-based Adversarial Networks (ICLR 2024 · similarity 0.91)
- Discriminator optimal transport (NeurIPS 2019 · similarity 0.91)
- On the Convergence and Robustness of Training GANs with Regularized Optimal Transport (NeurIPS 2018 · similarity 0.90)
- Projected GANs Converge Faster (NeurIPS 2021 · similarity 0.90)
- Generative Modeling with Optimal Transport Maps (ICLR 2022 · similarity 0.89)
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