EMoE: Training-Free Expert Disagreement for Uncertainty-Aware Text-to-Image Diffusion 事件

EMoE: Training-Free Expert Disagreement for Uncertainty-Aware Text-to-Image Diffusion arXiv:2505.13273v2 Announce Type: replace Abstract: Large text-to-image diffusion models rarely expose reliable signals of when a prompt is likely to produce a poorly aligned generation, especially when training data is undisclosed. We study whether expert disagreement inside pre-trained mixture-of-experts (MoE) diffusion models can serve as a reliable estimate for epistemic uncertainty. We introduce EMoE, a t