Title: Collaborative-Reverse Diffusion Models
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: Diffusion Model, Collaborative Modeling

Abstract: Given sufficient training samples, diffusion models have outperformed in various data generation tasks. However, natural science researches are often challenged by class imbalance and data sparseness. In order to apply the diffusion models to general natural science scenarios, we propose the Collaborative-Reverse Diffusion Models (CoReDM), a novel diffusion-based framework that incorporates a neural collaborative mechanism to improve the generating ability of sparse-class samples. We introduces a new paradigm for integrating the collective similarity into the reverse diffusion processes. The model employs a dual-network architecture consisting of a denoising network and a neural collaborative regularization term, which learns to assign influence weights between samples according to their collective relationships. This design facilitates adaptive knowledge transfer from dense to sparse classes during the denoising. Experimental results on several imbalanced datasets demonstrate that our CoReDM substantially enhances both the quality and diversity of generated samples for sparse classes, outperforming standard diffusion models and a few data augmentation baselines.