Decoupled Residual Denoising Diffusion Models for Unified and Data Efficient Image-to-Image Translation
We propose Decoupled Residual Denoising Diffusion models (DRDD) for unified and data-efficient image-to-image (I2I) translation. While diffusion models have advanced I2I translation in terms of quality and diversity, we uncover a previously under-explored property in diffusion models. Crucially, beyond its conventional role of manifold lifting (i.e., moving data off low-dimensional manifolds), injecting Gaussian noise facilitates domain harmonization by implicitly aligning feature distributions across domains, a property particularly advantageous for unified I2I translation. However, existing diffusion models prematurely erode this harmonization effect, as noise and residuals are simultan...
| Authors | Ziyue Lin, Jiahe Hou, Hongyu Xia et al. |
|---|---|
| Primary category | cs.CV |
| Published | 31 May 2026 |
| Last updated at source | 31 May 2026 |
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