Deep Generative Modeling with Backward Stochastic Differential Equations

• URL: http://arxiv.org/abs/2304.04049v1
• Date: Sat, 8 Apr 2023 15:37:38 GMT
• Title: Deep Generative Modeling with Backward Stochastic Differential Equations
• Authors: Xingcheng Xu
• Abstract summary: This paper proposes a novel deep generative model, called BSDE-Gen, which combines the flexibility of backward differential equations with the power of deep neural networks. The incorporation of the uncertainty in the generative modeling process makes BSDE-Gen an effective and natural approach for generating high-dimensional data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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