Pay Attention to MLPs Defa Zhu https://zhudefa.github.io/ 来自专栏 · AI时事追击 11 人赞同了该文章 一句话总结 最近几篇"Fully MLP"工作里面,性能最够看的工作。和MLP-Mixer和ResMLP关键的区别是,spatial-wise的FC得到的结果要和输入做乘积,类似Gating的操作,也是该工作方法名字gMLP的由来。结果...
multi-layer perceptrons (MLPs) to encode rich local patterns in the early stages while applying self-attention modules to capture longer dependencies in deeper layers. Moreover, we further propose a learned deformable token merging module to adaptively fuse informative patches in a non-uniform manner...
multi-head self-attention blocks, 可以聚合token之间的空间信息。 其中的attention mechanism一直被认为transformers取得优秀成绩的重要因素。和MLP相比,attention可以根据模型输入,调整参数,而MLP的参数是固定的。那么问题来了,transformers效果那么好,是self-attention起的决定性作用吗,self-attention是必要的吗? 本文提出了...
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