English(EN) On the Necessity of Attention-FFN Split in Vision Transformers

新的统一模块挑战标准Vision Transformer架构

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-08 04:00

研究人员引入了一个名为AttenFeed的新模块，它统一了Attention和前馈网络（FFN）层的功能。该模块用于创建一个统一的Vision Transformer（uViT），挑战了Vision Transformers（ViTs）中标准的交替Attention-FFN结构。实验表明，Attention和FFN层之间的严格分离可能会通过僵化地分配参数而对较小的ViT模型的性能产生负面影响。uViT为理解Attention-FFN结构提供了新的分析工具，并为传统的ViT架构提供了理论见解。 AI

影响引入了一个新的架构组件，可能导致更高效、性能更优的Vision Transformers，尤其是在较小规模的模型上。

排序理由学术论文，介绍了一种新颖的Vision Transformer模块和架构。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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arXiv cs.CV TIER_1 English(EN) · Junhyeok Kim, Jinyeong Kim, Jae Wan Park, Seong Jae Hwang · 2026-10-08 04:00

Vision Transformers 中 Attention-FFN 分离的必要性

arXiv:2610.10303v1 Announce Type: new Abstract: The standard Transformer architecture relies on a rigid pattern that alternates Attention and Feed-Forward Network (FFN) layers. Despite its widespread adoption, the inductive bias imposed by this strict separation has not been syst…

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Vision Transformers 中 Attention-FFN 分离的必要性

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