CountsDiff: A Diffusion Model on the Natural Numbers for Generation and Imputation of Count-Based Data
arXiv:2604.03779v1 Announce Type: new Abstract: Diffusion models have excelled at generative tasks for both continuous and token-based domains, but their application to discrete ordinal data remains underdeveloped. We present CountsDiff, a diffusion framework designed to natively model distributions on the natural numbers. CountsDiff extends the Blackout diffusion framework by simplifying its formulation through a direct parameterization in terms of a survival probability schedule and an explicit loss weighting. This introduces flexibility through design parameters with direct analogues in existing diffusion modeling frameworks. Beyond this reparameterization, CountsDiff introduces features from modern diffusion models, previously absent in counts-based domains, including continuous-time training, classifier-free guidance, and churn/remasking reverse dynamics that allow non-monotone reverse trajectories. We propose an initial instantiation of CountsDiff and validate it on natural imag
arXiv:2604.03779v1 Announce Type: new Abstract: Diffusion models have excelled at generative tasks for both continuous and token-based domains, but their application to discrete ordinal data remains underdeveloped. We present CountsDiff, a diffusion framework designed to natively model distributions on the natural numbers. CountsDiff extends the Blackout diffusion framework by simplifying its formulation through a direct parameterization in terms of a survival probability schedule and an explicit loss weighting. This introduces flexibility through design parameters with direct analogues in existing diffusion modeling frameworks. Beyond this reparameterization, CountsDiff introduces features from modern diffusion models, previously absent in counts-based domains, including continuous-time training, classifier-free guidance, and churn/remasking reverse dynamics that allow non-monotone reverse trajectories. We propose an initial instantiation of CountsDiff and validate it on natural image datasets (CIFAR-10, CelebA), exploring the effects of varying the introduced design parameters in a complex, well-studied, and interpretable data domain. We then highlight biological count assays as a natural use case, evaluating CountsDiff on single-cell RNA-seq imputation in a fetal cell and heart cell atlas. Remarkably, we find that even this simple instantiation matches or surpasses the performance of a state-of-the-art discrete generative model and leading RNA-seq imputation methods, while leaving substantial headroom for further gains through optimized design choices in future work.
Executive Summary
CountsDiff, a novel diffusion model, is introduced to tackle discrete ordinal data. It extends the Blackout diffusion framework with a simplified formulation, flexibility through design parameters, continuous-time training, classifier-free guidance, and reverse dynamics. CountsDiff is validated on natural image datasets (CIFAR-10, CelebA) and single-cell RNA-seq imputation. The model achieves competitive performance compared to state-of-the-art discrete generative models and leading RNA-seq imputation methods. The authors propose that CountsDiff can be applied to various count-based data domains, including biological count assays. This work provides a promising approach to modeling discrete ordinal data and has significant implications for data imputation and generation applications.
Key Points
- ▸ Introduction of CountsDiff, a novel diffusion model for discrete ordinal data
- ▸ Extension of the Blackout diffusion framework with simplified formulation and flexibility
- ▸ Validation of CountsDiff on natural image datasets and single-cell RNA-seq imputation
Merits
Strength in flexibility
CountsDiff introduces design parameters with direct analogues in existing diffusion modeling frameworks, offering flexibility in model design and potential for improved performance.
Competitive performance
CountsDiff achieves competitive performance compared to state-of-the-art discrete generative models and leading RNA-seq imputation methods, indicating its potential for practical applications.
Demerits
Limited exploration of design parameters
The authors only explore the effects of varying a few design parameters, leaving substantial headroom for further gains through optimized design choices in future work.
Limited evaluation on diverse datasets
CountsDiff is primarily evaluated on natural image datasets and single-cell RNA-seq imputation, limiting its generalizability to other domains.
Expert Commentary
CountsDiff is a promising approach to modeling discrete ordinal data, offering flexibility and competitive performance compared to state-of-the-art models. However, further research is needed to fully explore its potential and evaluate its performance on diverse datasets. Additionally, the application of CountsDiff to various count-based data domains, such as biological count assays, warrants further investigation. Overall, this work contributes to the ongoing development of diffusion models and has significant implications for data imputation and generation applications.
Recommendations
- ✓ Future research should focus on exploring the effects of varying design parameters to optimize model performance.
- ✓ CountsDiff should be evaluated on a broader range of datasets to assess its generalizability and potential for practical applications.
Sources
Original: arXiv - cs.LG