Research map / Architectures and efficiency / RNNs and normalization
Why Spectral Normalization Stabilizes GANs: Analysis and Improvements
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add scaling to SN weights (ablation) | SN · CIFAR10 | IS: 7.12 $\pm$ 0.07 -> 7.38 $\pm$ 0.06 | Helped |
| add bidirectional normalization to SN (ablation) | SN · CIFAR10 | FID: 31.43 $\pm$ 0.90 -> 26.94 $\pm$ 0.58 | Helped |
| add bidirectional normalization to SN (ablation) | SN · STL10 | FID: 44.35 $\pm$ 0.54 -> 42.98 $\pm$ 0.54 | Helped |
| add bidirectional normalization to SN (ablation) | SN · CelebA | FID: 9.43 $\pm$ 0.09 -> 9.05 $\pm$ 0.13 | Helped |
| add bidirectional normalization to SN (ablation) | SN · ILSVRC2012 | IS: 12.84 $\pm$ 0.33 -> 1.77 $\pm$ 0.13 | Hurt |
| add scaling to BSN (ablation) | BSN · ILSVRC2012 | IS: 1.77 $\pm$ 0.13 -> 13.23 $\pm$ 0.16 | Helped |
| add scaling to BSN (ablation) | BSN · CIFAR10 | IS: 7.54 $\pm$ 0.04 -> 7.54 $\pm$ 0.04 | No clear effect |
| add bidirectional normalization and scaling (ablation) | SN · ILSVRC2012 | FID: 75.06 $\pm$ 2.38 -> 69.04 $\pm$ 1.46 | Helped |
Closest papers by meaning
- Spectral Normalization for Generative Adversarial Networks (ICLR 2018 · similarity 0.94)
- Sparsity Aware Normalization for GANs (AAAI 2021 · similarity 0.92)
- A Large-Scale Study on Regularization and Normalization in GANs (ICML 2019 · similarity 0.90)
- On Computation and Generalization of Generative Adversarial Networks under Spectrum Control (ICLR 2019 · similarity 0.90)
- Generalizable Adversarial Training via Spectral Normalization (ICLR 2019 · similarity 0.89)
- CHAIN: Enhancing Generalization in Data-Efficient GANs via lipsCHitz continuity constrAIned Normalization (CVPR 2024 · similarity 0.89)
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