English(EN) Beyond Routing: Characterising Expert Tuning and Representation in Vision Mixture-of-Experts

视觉MoE模型显示出稳定的动/静物专家专业化

作者 PulseAugur 编辑部 · [2 个来源] · 2026-05-20 01:55

研究人员开发了新的方法来分析计算机视觉中混合专家（MoE）模型的内部工作机制。他们的工作超越了简单地检查数据如何被路由到模型内的特定“专家”，而是专注于每个专家实际编码的内容。研究发现，动/静物区分是专家划分的主要因素，并且这种专业化在不同的模型初始化中是稳定的。 AI

影响提供了对视觉MoE模型内部表征的更深入见解，可能导致更具可解释性和鲁棒性的AI系统。

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arXiv cs.AI TIER_1 English(EN) · Gene Tangtartharakul, Katherine R. Storrs · 2026-05-22 04:00

超越路由：视觉混合专家中的专家调优与表征表征

arXiv:2605.20610v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models are often interpreted by analysing which categories are routed to which experts. However, routing alone does not reveal what each expert actually encodes. We train sparsely-gated convolutional MoE m…
arXiv cs.AI TIER_1 English(EN) · Katherine R. Storrs · 2026-05-20 01:55

超越路由：视觉混合专家中的专家调优与表征表征

Mixture-of-Experts (MoE) models are often interpreted by analysing which categories are routed to which experts. However, routing alone does not reveal what each expert actually encodes. We train sparsely-gated convolutional MoE models with a contrastive objective on natural imag…