Research map / Image generation / GANs
Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add mode-seeking regularization to DCGAN | DCGAN · CIFAR-10 | FID: 29.65\pm{0.06} -> 28.73\pm{0.06} | Helped |
| Add mode-seeking regularization to Pix2Pix | Pix2Pix · Facades | LPIPS: 0.0003\pm{0.0000} -> 0.1894\pm{0.0011} | Helped |
| Add mode-seeking regularization to Pix2Pix | Pix2Pix · Maps | LPIPS: 0.0016\pm{0.0003} -> 0.2189\pm{0.0004} | Helped |
| Add mode-seeking regularization to DRIT | DRIT · Dog2Cat | LPIPS: 0.102\pm{0.001} -> 0.214\pm{0.001} | Helped |
| Add mode-seeking regularization to StackGAN++ | StackGAN++ · CUB-200-2011, fixed text codes | LPIPS: 0.156\pm{0.004} -> 0.207\pm{0.005} | Helped |
| Increase λms from 0 to 1 (ablation) | Pix2Pix with mode-seeking regularization · Facades | LPIPS: 0.0003 -> 0.1888 | Helped |
| Increase λms from 1 to 3 (ablation) | Pix2Pix with mode-seeking regularization · Facades | FID: 92.45 -> 338.45 | Hurt |
| Replace L1 image distance with discriminator feature distance (ablation) | Pix2Pix with mode-seeking regularization · Facades | LPIPS: 0.1894\pm{0.0011} -> 0.0565\pm{0.0003} | Hurt |
Closest papers by meaning
- Hierarchical Modes Exploring in Generative Adversarial Networks (AAAI 2020 · similarity 0.95)
- Diversity-Sensitive Conditional Generative Adversarial Networks (ICLR 2019 · similarity 0.95)
- Large Scale GAN Training for High Fidelity Natural Image Synthesis (ICLR 2019 · similarity 0.90)
- Mode Regularized Generative Adversarial Networks (ICLR 2017 · similarity 0.89)
- Robust Conditional Generative Adversarial Networks (ICLR 2019 · similarity 0.89)
- Attack Deterministic Conditional Image Generative Models for Diverse and Controllable Generation (AAAI 2024 · similarity 0.87)
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