Abstract
Diffusion models excel at generating high-quality outputs but face challenges in data-scarce domains, where exhaustive retraining or costly paired data are often required. To address these limitations, we propose Latent Aligned Diffusion Bridges (LADB), a semi-supervised framework for sample-to-sample translation that effectively bridges domain gaps using partially paired data. By aligning source and target distributions within a shared latent space, LADB seamlessly integrates pretrained source-domain diffusion models with a target-domain Latent Aligned Diffusion Model (LADM), trained on partially paired latent representations. This approach enables deterministic domain mapping without the need for full supervision. Compared to unpaired methods, which often lack controllability, and fully paired approaches that require large, domain-specific datasets, LADB strikes a balance between fidelity and diversity by leveraging a mixture of paired and unpaired latent-target couplings. Our experimental results demonstrate superior performance in depth-to-image translation under partial supervision. Furthermore, we extend LADB to handle multi-source translation (from depth maps and segmentation masks) and multi-target translation in a class-conditioned style transfer task, showcasing its versatility in handling diverse and heterogeneous use cases. Ultimately, we present LADB as a scalable and versatile solution for real-world domain translation, particularly in scenarios where data annotation is costly or incomplete.
Abstract (translated)
扩散模型在生成高质量输出方面表现出色，但在数据稀疏领域却面临挑战，这些领域往往需要进行详尽的再训练或耗费大量资金获取配对数据。为解决这些问题，我们提出了一个半监督框架——潜空间对齐扩散桥接（Latent Aligned Diffusion Bridges, LADB），该框架通过使用部分配对的数据有效地弥合不同域之间的差距，用于样本到样本的转换任务。LADB 通过在共享的潜在空间中对齐源域和目标域分布，无缝地将预训练的源域扩散模型与基于部分配对潜在表示的目标域潜空间对齐扩散模型（Latent Aligned Diffusion Model, LADM）结合在一起。这种方法能够在无需全面监督的情况下实现确定性的领域映射。 与缺乏可控性的非配对方法相比，以及需要大量特定领域数据集的完全配对方法相比，LADB 通过利用配对和未配对潜在目标耦合的混合方式，在保真度和多样性之间取得了平衡。我们的实验结果表明，在部分监督下的深度到图像转换任务中，LADB 表现出色。此外，我们将 LADB 扩展至处理多源翻译（来自深度图和分割掩码）以及在条件样式迁移任务中的多目标翻译，展示了其在处理多样且异构应用场景时的灵活性。 最终，我们提出 LADB 作为一个可扩展且灵活的解决方案，用于解决现实世界领域的转换问题，特别是那些数据标注成本高昂或不完整的场景。
URL
https://arxiv.org/abs/2509.08628
