Deep Generative Modeling with Backward Stochastic Differential Equations

Xingcheng Xu

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arXiv · 2304.04049

Deep Generative Modeling with Backward Stochastic Differential Equations

Xingcheng Xu

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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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BibTeX RIS

Xingcheng Xu. 2023-04-08. Deep Generative Modeling with Backward Stochastic Differential Equations. https://arxiv.org/abs/2304.04049

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