OT-ALD：基于最优传输对齐潜在分布以加速图像到图像翻译
OT-ALD: Aligning Latent Distributions with Optimal Transport for Accelerated Image-to-Image Translation
AI总结：
针对DDIB图像翻译方法效率低、潜在分布不匹配导致轨迹偏移的问题，本文提出基于最优传输的OT-ALD框架，可对齐潜在分布，在提升翻译速度的同时改善生成质量，实验验证其性能优于现有最优基线。
摘要：
双扩散隐式桥（DDIB）是一种新兴的图像到图像（I2I）翻译方法，在保持循环一致性的同时具备极强的灵活性。该方法通过先对源域图像加噪得到潜在编码，再在目标域对其去噪生成翻译图像，从而连接源域与目标域两个独立训练的扩散模型（DM）。但该方法面临两大核心挑战：一是翻译效率低下，二是潜在分布不匹配导致的翻译轨迹偏移。为解决上述问题，本文提出一种基于最优传输（OT）理论的新型I2I翻译框架OT-ALD，保留了基于DDIB方法的优势。具体而言，我们计算从源域潜在分布到目标域潜在分布的OT映射，并将映射后的分布作为目标域反向扩散过程的起点。误差分析证实OT-ALD消除了潜在分布不匹配问题，且该方法可有效平衡更快的图像翻译速度与更高的图像质量。在三个高分辨率数据集的四个翻译任务上的实验表明，与性能最优的基线模型相比，OT-ALD平均将采样效率提升20.29%，FID分数平均降低2.6。
英文摘要：
The Dual Diffusion Implicit Bridge (DDIB) is an emerging image-to-image (I2I) translation method that preserves cycle consistency while achieving strong flexibility. It links two independently trained diffusion models (DMs) in the source and target domains by first adding noise to a source image to obtain a latent code, then denoising it in the target domain to generate the translated image. However, this method faces two key challenges: (1) low translation efficiency, and (2) translation trajectory deviations caused by mismatched latent distributions. To address these issues, we propose a novel I2I translation framework, OT-ALD, grounded in optimal transport (OT) theory, which retains the strengths of DDIB-based approach. Specifically, we compute an OT map from the latent distribution of the source domain to that of the target domain, and use the mapped distribution as the starting point for the reverse diffusion process in the target domain. Our error analysis confirms that OT-ALD eliminates latent distribution mismatches. Moreover, OT-ALD effectively balances faster image translation with improved image quality. Experiments on four translation tasks across three high-resolution datasets show that OT-ALD improves sampling efficiency by 20.29% and reduces the FID score by 2.6 on average compared to the top-performing baseline models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
