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谷歌大脑最新操作玩“复古”：不用卷积注意力，图像分类接近SOTA

来源：说长论短网编辑：热点时间：2026-11-14

本文经AI新媒体量子位（公众号ID:QbitAI）授权转载，转谷歌载请联系出处。

谷歌大脑的视觉Transformer团队（ViT），搞了个大脑复古操作。

他们不用卷积神经网络（CNN）、也不用Transformer最新，仅凭最早的AI视觉任务采用的多层感知机（MLP）结构，就实现了操作接近SOTA的性能，更是在ImageNet图像分类任务上取得了87.玩“94%的准确率。

这个架构名为MLP-Mixer，采用两种不同类型的复古MLP层，可以看做是一个特殊的CNN，使用 1×1卷积进行通道混合”：（按位操作），同时全感受野和参数共享的的单通道深度卷积进行字符不用混合（跨位操作）。

在JFT-300M数据集上预训练、微调到224分辨卷积率的Mixer-H/14版本取得了86.32%的准确率，比SO注意TA模型ViT-H/14仅低0.3%，但运行速度是其2.2倍。

论文地址：
h力，ttps://arxiv.org/abs/2105.01601

项图像目地址：
https://github.com/google-resear分类ch/vision_transformer/tree/line接近n

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