Mitigating Diffusion Model Hallucinations with Dynamic Guidance 事件

Mitigating Diffusion Model Hallucinations with Dynamic Guidance arXiv:2510.05356v2 Announce Type: replace Abstract: Hallucinations in diffusion models are samples with structural inconsistencies that can emerge due to the excessive smoothing of the learned score function, which in turn leads to interpolations between modes of the data distribution. Since semantic interpolations are often desirable and contribute to sample diversity, we believe that a nuanced and targeted solution is required to