Research map / Image generation / Editing and style transfer
Marginal Contrastive Correspondence for Guided Image Generation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add correspondence (COR) to SPADE (ablation) | SPADE · CelebA-HQ | FID: 32.02 -> 15.77 | Helped |
| Add marginal contrastive loss (MCL) to COR (ablation) | MCL-Net correspondence · CelebA-HQ | FID: 15.77 -> 12.75 | Helped |
| Add self-correlation map (SCM) to COR (ablation) | MCL-Net correspondence · CelebA-HQ | FID: 15.77 -> 13.27 | Helped |
| Add both MCL and SCM to COR (ablation) | MCL-Net correspondence · CelebA-HQ | FID: 15.77 -> 12.52 | Helped |
| Add MCL with increasing margin m from 0.1 to 0.4 (ablation) | CoCosNet correspondence · DeepFashion warped exemplars | L1: 79.32 -> 75.27 | Helped |
| Add SCM to CoCosNet correspondence (ablation) | CoCosNet correspondence · DeepFashion warped exemplars | L1: 80.14 -> 76.07 | Helped |
| Combine MCL (m=0.4) and SCM (ablation) | CoCosNet correspondence · DeepFashion warped exemplars | L1: 80.14 -> 73.72 | Helped |
| Use a large MCL margin such as m=0.5 (ablation) | MCL-Net · DeepFashion | convergence | Hurt |
| Use MCL-Net instead of CoCosNet v2 | Image translation · CelebA-HQ | FID: 12.85 -> 12.52 | Helped |
| Use MCL-Net instead of CoCosNet v2 | Image translation · DeepFashion | SWD: 16.53 -> 16.24 | Helped |
| Use MCL-Net instead of CoCosNet v2 | Image translation · ADE20K | LPIPS: 0.564 -> 0.569; UNITE: 0.571 | Hurt |
Closest papers by meaning
- Cross-Domain Correspondence Learning for Exemplar-Based Image Translation (CVPR 2020 · similarity 0.93)
- Masked and Adaptive Transformer for Exemplar Based Image Translation (CVPR 2023 · similarity 0.89)
- CoCosNet v2: Full-Resolution Correspondence Learning for Image Translation (CVPR 2021 · similarity 0.88)
- Exemplar Guided Unsupervised Image-to-Image Translation with Semantic Consistency (ICLR 2019 · similarity 0.88)
- FashionR2R: Texture-preserving Rendered-to-Real Image Translation with Diffusion Models (NeurIPS 2024 · similarity 0.86)
- Learning to Manipulate Artistic Images (AAAI 2024 · similarity 0.85)
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