Sparse Data Generation Using Diffusion Models

• URL: http://arxiv.org/abs/2502.02448v1
• Date: Tue, 04 Feb 2025 16:14:28 GMT
• Title: Sparse Data Generation Using Diffusion Models
• Authors: Phil Ostheimer, Mayank Nagda, Marius Kloft, Sophie Fellenz,
• Abstract summary: We introduce Sparse Data Diffusion (SDD), a novel method for generating sparse data. SDD achieves high fidelity in representing data sparsity while preserving the quality of the generated data.
• Score: 22.560860958917672
• License:
• Abstract: Sparse data is ubiquitous, appearing in numerous domains, from economics and recommender systems to astronomy and biomedical sciences. However, efficiently and realistically generating sparse data remains a significant challenge. We introduce Sparse Data Diffusion (SDD), a novel method for generating sparse data. SDD extends continuous state-space diffusion models by explicitly modeling sparsity through the introduction of Sparsity Bits. Empirical validation on image data from various domains-including two scientific applications, physics and biology-demonstrates that SDD achieves high fidelity in representing data sparsity while preserving the quality of the generated data.

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