Opportunities and challenges of diffusion models for generative AI.

ABSTRACT: Diffusion models, a powerful and universal generative artificial intelligence technology, have achieved tremendous success and opened up new possibilities in diverse applications. In these applications, diffusion models provide flexible high-dimensional data modeling, and act as a sampler for generating new samples under active control towards task-desired properties. Despite the significant empirical success, theoretical underpinnings of diffusion models are very limited, potentially slowing down principled methodological innovations for further harnessing and improving diffusion models. In this paper, we review emerging applications of diffusion models to highlight their sample generation capabilities under various control goals. At the same time, we dive into the unique working flow of

SUBMITTER: Chen M 

PROVIDER: S-EPMC11562846 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature