Sparse Data Generation Using Diffusion Models

SDD extends Despite significant advances in generative modeling, a critical continuous state-space diffusion models by explicitly gap remains in developing models explicitly designed modeling sparsity through the introduction of for sparse data. Directly generating sparse data ensures that Sparsity Bits. Empirical validation on image data models learn realistic structures and distributions, preserving from various domains--including two scientific meaningful relationships that thresholding dense data applications, physics and biology--demonstrates would distort. Sparse data is crucial for applications like that SDD achieves high fidelity in representing data augmentation, where realistic but varied samples improve data sparsity while preserving the quality of the model robustness, and compressed representations, generated data.