Applying Regularized Schr\"odinger-Bridge-Based Stochastic Process in Generative Modeling. (arXiv:2208.07131v1 [cs.LG])

Compared to the existing function-based models in deep generative modeling,
the recently proposed diffusion models have achieved outstanding performance
with a stochastic-process-based approach. But a long sampling time is required
for this approach due to many timesteps for discretization. Schr\"odinger
bridge (SB)-based models attempt to tackle this problem by training
bidirectional stochastic processes between distributions. However, they still
have a slow sampling speed compared to generative models such as generative
adversarial networks. And due to the training of the bidirectional …

arxiv bridge lg modeling process stochastic stochastic process