Methods › Computer Vision › Pooling Operations › Class-MLP
Class-MLP
Introduced by Hugo Touvron et al. in ResMLP: Feedforward networks for image classification with data-efficient training
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Class-MLP is an alternative to average pooling, which is an adaptation of the class-attention token introduced in CaiT. In CaiT, this consists of two layers that have the same structure as the transformer, but in which only the class token is updated based on the frozen patch embeddings. In Class-MLP, the same approach is used, but after aggregating the patches with a linear layer, we replace the attention-based interaction between the class and patch embeddings by simple linear layers, still keeping the patch embeddings frozen. This increases the performance, at the expense of adding some parameters and computational cost. This pooling variant is referred to as “class-MLP”, since the purpose of these few layers is to replace average pooling.
Papers archive 2025-07-28
3 shown of 3, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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PRKAN: Parameter-Reduced Kolmogorov-Arnold Networks 13 Jan 2025 · 1 repository · arXiv:2501.07032
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Analyzing sports commentary in order to automatically recognize events and extract insights 18 Jul 2023 · 2 repositories · arXiv:2307.10303
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ResMLP: Feedforward networks for image classification with data-efficient training 7 May 2021 · 19 repositories · arXiv:2105.03404Syntology ran 2 of 7 samples · 5 unverified
Tasks archive 2025-07-28
11 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
Usage over time archive 2025-07-28
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Categories archive 2025-07-28
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