Dominating vs. Dominated: Generative Collapse in Diffusion Models

This article likely discusses the phenomenon of generative collapse within diffusion models, a critical issue in AI research. Generative collapse refers to the tendency of these models to produce a limited variety of outputs, often focusing on a small subset of the training data. The title suggests an exploration of the dynamics of this collapse, potentially analyzing factors that contribute to it (dominating) and the consequences (dominated). The source, ArXiv, indicates this is a research paper, suggesting a technical and in-depth analysis.