Research map / Image generation / GANs
First Order Generative Adversarial Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace WGAN-GP with FOGAN | DCGAN image generator · CelebA | FID: 4.2 -> 6.0 | Hurt |
| Replace WGAN-GP with FOGAN | DCGAN image generator · LSUN | FID: 9.5 -> 11.4 | Hurt |
| Replace WGAN-GP with FOGAN | image generator · CIFAR-10 | FID: 24.8 -> 27.4 | Hurt |
| Replace WGAN-GP with FOGAN | 1D CNN language generator · One Billion Word 4-gram | JSD: $.220\pm .006$ -> $.226\pm .006$ | Hurt |
| Replace WGAN-GP with FOGAN | 1D CNN language generator · One Billion Word 6-gram | JSD: $.573\pm .009$ -> $.556 \pm .004$ | Helped |
Closest papers by meaning
- Training Language GANs from Scratch (NeurIPS 2019 · similarity 0.90)
- Training Generative Adversarial Networks in One Stage (CVPR 2021 · similarity 0.89)
- Are GANs overkill for NLP? (NeurIPS 2022 · similarity 0.89)
- TRAINING GENERATIVE ADVERSARIAL NETWORKS VIA PRIMAL-DUAL SUBGRADIENT METHODS: A LAGRANGIAN PERSPECTIVE ON GAN (ICLR 2018 · similarity 0.89)
- Training Generative Adversarial Networks with Limited Data (NeurIPS 2020 · similarity 0.89)
- A Large-Scale Study on Regularization and Normalization in GANs (ICML 2019 · similarity 0.89)
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