Sparse Data Generation Using Diffusion Models

2025-02-04

AI Summary

Sparse data generation has long confronted the challenge of balancing fidelity and efficiency in domains including economics, recommender systems, astronomy, and biomedical research. To address this, we propose Sparse Data Diffusion (SDD), the first diffusion-based generative model to explicitly encode sparsity structure within a continuous-time diffusion framework via a novel “Sparsity Bits” mechanism—jointly optimizing sparsity preservation and semantic fidelity. Our method integrates binary sparse mask learning, hierarchical noise scheduling, and domain-adaptive reconstruction loss. Experiments on physics and bioscience imaging datasets demonstrate that SDD significantly improves both sample quality and sparsity pattern authenticity: FID decreases by 23%, sparsity error reduces by 41%, and—critically—it achieves, for the first time in diffusion modeling, coherent co-generation of sparse structural patterns and semantically meaningful content.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Web Mining and Content Analysis: Content-based information diffusion

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.

Problem

• Generates sparse data efficiently
• Models sparsity with Sparsity Bits
• Validates on scientific image data

Innovation

• Sparse Data Diffusion introduced
• Sparsity Bits model explicitly
• High fidelity in sparsity preservation