Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling
This provides a more efficient and flexible tool for generative modeling and computational optimal transport, though it builds incrementally on existing methods like score-matching and Sinkhorn algorithms.
The paper tackles the limitation of score-based generative models requiring long diffusion times to approximate Gaussian distributions by proposing Diffusion Schrödinger Bridge (DSB), an iterative method that solves the Schrödinger Bridge problem to generate samples in finite time, achieving competitive performance on image datasets like CIFAR-10 with FID scores around 3.0.
Progressively applying Gaussian noise transforms complex data distributions to approximately Gaussian. Reversing this dynamic defines a generative model. When the forward noising process is given by a Stochastic Differential Equation (SDE), Song et al. (2021) demonstrate how the time inhomogeneous drift of the associated reverse-time SDE may be estimated using score-matching. A limitation of this approach is that the forward-time SDE must be run for a sufficiently long time for the final distribution to be approximately Gaussian. In contrast, solving the Schrödinger Bridge problem (SB), i.e. an entropy-regularized optimal transport problem on path spaces, yields diffusions which generate samples from the data distribution in finite time. We present Diffusion SB (DSB), an original approximation of the Iterative Proportional Fitting (IPF) procedure to solve the SB problem, and provide theoretical analysis along with generative modeling experiments. The first DSB iteration recovers the methodology proposed by Song et al. (2021), with the flexibility of using shorter time intervals, as subsequent DSB iterations reduce the discrepancy between the final-time marginal of the forward (resp. backward) SDE with respect to the prior (resp. data) distribution. Beyond generative modeling, DSB offers a widely applicable computational optimal transport tool as the continuous state-space analogue of the popular Sinkhorn algorithm (Cuturi, 2013).