This paper introduces a novel theoretical simplification of the Diffusion
Schr\"odinger Bridge (DSB) that facilitates its unification with Score-based
Generative Models (SGMs), addressing the limitations of DSB in complex data
generation and enabling faster convergence and enhanced performance. By
employing SGMs as an initial solution for DSB, our approach capitalizes on the
strengths of both frameworks, ensuring a more efficient training process and
improving the performance of SGM. We also propose a reparameterization
technique that, despite theoretical approximations, practically improves the
network's fitting capabilities. Our extensive experimental evaluations confirm
the effectiveness of the simplified DSB, demonstrating its significant
improvements. We believe the contributions of this work pave the way for
advanced generative modeling. The code is available at
this https URL

本研究介绍了一种新颖的理论简化方法，将扩散薛定谔桥与基于评分的生成模型统一起来，解决了扩散薛定谔桥在复杂数据生成方面的局限性，实现了更快的收敛和更强的性能。通过将基于评分的生成模型作为扩散薛定谔桥的初始解决方案，我们的方法充分发挥了两个框架的优势，确保了更高效的训练过程并提高了基于评分的生成模型的性能。同时，我们还提出了一种重新参数化技术，尽管在理论上存在逼近，但在实践中可以提高网络的适应能力。我们进行了广泛的实验评估，证实了简化扩散薛定谔桥的有效性，并展示了其显著的改进。我们相信本研究的贡献为先进的生成建模铺平了道路。代码可在此 https URL 获取。