  This paper proposes a novel deep generative model, called BSDE-Gen, which
combines the flexibility of backward stochastic differential equations (BSDEs)
with the power of deep neural networks for generating high-dimensional complex
target data, particularly in the field of image generation. The incorporation
of stochasticity and uncertainty in the generative modeling process makes
BSDE-Gen an effective and natural approach for generating high-dimensional
data. The paper provides a theoretical framework for BSDE-Gen, describes its
model architecture, presents the maximum mean discrepancy (MMD) loss function
used for training, and reports experimental results.
