Research map / Image generation / GANs
Surrogate Gradient Field for Latent Space Manipulation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use SGF instead of InterfaceGAN for facial-attribute manipulation | StyleGAN2 · FFHQ-Attributes: Overall | MDS: 0.721 -> 0.837 | Helped |
| Use SGF instead of GANSpace for facial-attribute manipulation | StyleGAN2 · FFHQ-Attributes: Overall | MDS: 0.531 -> 0.837 | Helped |
| Use SGF instead of InterfaceGAN for facial-attribute manipulation | ProgressiveGAN · CelebAHQ-Attributes: Overall | MDS: 0.758 -> 0.876 | Helped |
| Add keypoints and attributes together as conditioning variables, rather than using keypoints alone (ablation) | SGF · Anime-KeypointsAttr | hair-color preservation | Helped |
| Use W-space instead of Z-space (ablation) | SGF on StyleGAN2 · FFHQ-Attributes | MDS: 0.807 -> 0.837 | Helped |
| Decrease step size from λ = 1 to λ = 0.2 (ablation) | SGF in W-space · FFHQ-Attributes: Overall | MDS: 0.812 -> 0.837 | Helped |
| Decrease step size from λ = 0.2 to λ = 0.02 (ablation) | SGF in W-space · FFHQ-Attributes: Overall | MDS: 0.837 -> 0.306 | Hurt |
| Decrease step size from λ = 0.2 to λ = 0.02 (ablation) | SGF in Z-space · FFHQ-Attributes: Overall | MDS: 0.807 -> 0.198 | Hurt |
| Increase the manipulation step size (ablation) | SGF · FFHQ-Attributes | accuracy | Mixed |
| Replace per-step condition re-estimation with the estimated condition increment (ablation) | SGF fast version · FFHQ-Attributes, Anime-KeypointsAttr, Flowers-Caption | running time: 2.89 -> 0.304; 6.54 -> 0.332; 11.73 -> 0.366 | Helped |
| Use SGF rather than InterfaceGAN for user-preferred disentanglement | FFHQ facial-attribute editing · User study | preference share: 61% | Helped |
| Apply a linear direction in place of SGF's non-linear path (ablation) | SGF manipulation · Anime-KeypointsAttr | editing quality | Mixed |
Closest papers by meaning
- Interpreting the Latent Space of GANs for Semantic Face Editing (CVPR 2020 · similarity 0.91)
- Enjoy Your Editing: Controllable GANs for Image Editing via Latent Space Navigation (ICLR 2021 · similarity 0.90)
- Exploiting Spatial Dimensions of Latent in GAN for Real-Time Image Editing (CVPR 2021 · similarity 0.89)
- Using latent space regression to analyze and leverage compositionality in GANs (ICLR 2021 · similarity 0.89)
- Navigating the GAN Parameter Space for Semantic Image Editing (CVPR 2021 · similarity 0.89)
- Low-Rank Subspaces in GANs (NeurIPS 2021 · similarity 0.89)
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