Simplified Diffusion Schr\"odinger Bridge

Understand

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.