  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 stochastic
processes, they require a relatively long training time. Therefore, this study
tried to reduce the number of timesteps and training time required and proposed
regularization terms to the existing SB models to make the bidirectional
stochastic processes consistent and stable with a reduced number of timesteps.
Each regularization term was integrated into a single term to enable more
efficient training in computation time and memory usage. Applying this
regularized stochastic process to various generation tasks, the desired
translations between different distributions were obtained, and accordingly,
the possibility of generative modeling based on a stochastic process with
faster sampling speed could be confirmed. The code is available at
https://github.com/KiUngSong/RSB.
