Research map / Label-efficient and robust learning / Self-supervised and contrastive
GAN-Supervised Dense Visual Alignment
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use GANgealing instead of the strongest listed baseline | SPair-71K Bicycle correspondence · SPair-71K Bicycle test | PCK-Transfer@α_bbox=0.1: 34.7 -> 37.5 | Helped |
| use GANgealing instead of the strongest listed baseline | SPair-71K Cat correspondence · SPair-71K Cat test | PCK-Transfer@α_bbox=0.1: 66.5 -> 67.0 | Helped |
| use GANgealing instead of the strongest listed baseline | SPair-71K Dog correspondence · SPair-71K Dog test | PCK-Transfer@α_bbox=0.1: 24.3 -> 23.1 | Hurt |
| use GANgealing instead of the strongest listed baseline | SPair-71K TV correspondence · SPair-71K TV test | PCK-Transfer@α_bbox=0.1: 58.0 -> 57.9 | Hurt |
| use GANgealing instead of the strongest correspondence-supervised baseline | CUB correspondence · CUB validation | PCK-Transfer@0.1: 58.5 -> 57.5 | Hurt |
| learn the target latent rather than fixing it to the average (ablation) | GANgealing · SPair-71K Cats | PCK@α_bbox=0.1: 10.6 -> 67.2 | Helped |
| constrain target optimization to 1 PCA coefficient instead of 512 (ablation) | GANgealing · SPair-71K Cats | PCK@α_bbox=0.1: 0.34 -> 67.2 | Helped |
| set the style-mixing cutoff to 4 rather than 5 (ablation) | GANgealing · SPair-71K Cats | PCK@α_bbox=0.1: 67.2 -> 60.5 | Hurt |
| set the style-mixing cutoff to 6 rather than 5 (ablation) | GANgealing · SPair-71K Cats | PCK@α_bbox=0.1: 67.2 -> 65.0 | Hurt |
| remove style mixing from per-input targets (ablation) | GANgealing · SPair-71K Cats | PCK@α_bbox=0.1: 67.2 -> 25.9 | Hurt |
| remove style mixing and use LPIPS loss (ablation) | GANgealing · SPair-71K Cats | PCK@α_bbox=0.1: 67.0 -> 7.74 | Hurt |
| remove total variation flow regularization (ablation) | GANgealing · SPair-71K Cats | PCK@α_bbox=0.1: 67.2 -> 59.0 | Hurt |
Closest papers by meaning
- CoordGAN: Self-Supervised Dense Correspondences Emerge From GANs (CVPR 2022 · similarity 0.90)
- DenseReg: Fully Convolutional Dense Shape Regression In-The-Wild (CVPR 2017 · similarity 0.87)
- PandA: Unsupervised Learning of Parts and Appearances in the Feature Maps of GANs (ICLR 2023 · similarity 0.87)
- LARGE: Latent-Based Regression Through GAN Semantics (CVPR 2022 · similarity 0.87)
- Ensembling Off-the-Shelf Models for GAN Training (CVPR 2022 · similarity 0.87)
- E$^2$GAN: Efficient Training of Efficient GANs for Image-to-Image Translation (ICML 2024 · similarity 0.86)
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