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.

翻译：本文提出了一种称为BSDE-Gen的新型深度生成模型，它将反向随机微分方程（BSDEs）的灵活性与深度神经网络的强大能力相结合，用于生成高维复杂目标数据，特别是在图像生成领域。将随机性和不确定性融入到生成建模过程中，使得BSDE-Gen成为一种有效而自然的生成高维数据的方法。本文提供了BSDE-Gen的理论框架，详细描述了其模型架构，介绍了用于训练的最大平均差异（MMD）损失函数，并报告了实验结果。