Research map / Image generation / Editing and style transfer
Homomorphic Latent Space Interpolation for Unpaired Image-To-Image Translation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use homomorphic latent-space interpolation for image translation | Ours · CelebA Turing Test | edited image judged real (%): 40% vs 33% | Helped |
| Use homomorphic latent-space interpolation for image translation | Ours · CelebA A/B Test vs StarGAN | Ours preferred (%): 61% | Helped |
| Use homomorphic latent-space interpolation for image translation | Ours · CelebA A/B Test vs ELEGANT | Ours preferred (%): 69% | Helped |
| Use homomorphic latent-space interpolation for image translation | Ours · CelebA A/B Test vs Facelet | Ours preferred (%): 72% | Helped |
| Add rigorous training for the attribute classifier and interpolator (ablation) | Ours · CelebA attribute translation | attribute-edit quality | Helped |
| Add VGG-based knowledge guidance to the encoder (ablation) | Ours · CelebA attribute translation | image realism | Helped |
| Add the homomorphic loss (ablation) | Ours · CelebA attribute translation | target-attribute transfer | Helped |
Closest papers by meaning
- Smoothing the Disentangled Latent Style Space for Unsupervised Image-to-Image Translation (CVPR 2021 · similarity 0.91)
- Unsupervised Image-to-Image Translation With Generative Prior (CVPR 2022 · similarity 0.89)
- Multi-mapping Image-to-Image Translation via Learning Disentanglement (NeurIPS 2019 · similarity 0.89)
- Latent Filter Scaling for Multimodal Unsupervised Image-To-Image Translation (CVPR 2019 · similarity 0.89)
- Breaking the Cycle - Colleagues Are All You Need (CVPR 2020 · similarity 0.88)
- Unsupervised Image-to-Image Translation Networks (NeurIPS 2017 · similarity 0.88)
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