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：本文提出一种名为BSDE-Gen的新型深度生成模型，该模型将倒向随机微分方程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，特别在图像生成领域。生成建模过程中对随机性与不确定性的融入，使BSDE-Gen成为生成高维数据的有效且自然的途径。本文为BSDE-Gen提供了理论框架，描述了其模型架构，介绍了用于训练的最大均值差异损失函数，并报告了实验结果。