{"url":"/method/gmlp","slug":"gmlp","name":"gMLP","full_name":"gMLP","full_name_withheld":false,"description_markdown":"**gMLP** is an [MLP](https://paperswithcode.com/methods/category/feedforward-networks)-based alternative to [Transformers](https://paperswithcode.com/methods/category/vision-transformer) without [self-attention](https://paperswithcode.com/method/scaled), which simply consists of channel projections and spatial projections with static parameterization. It is built out of basic MLP layers with gating. The model consists of a stack of $L$ blocks with identical size and structure. Let $X \\in \\mathbb{R}^{n \\times d}$ be the token representations with sequence length $n$ and dimension $d$. Each block is defined as:\r\n\r\n$$\r\nZ=\\sigma(X U), \\quad \\tilde{Z}=s(Z), \\quad Y=\\tilde{Z} V\r\n$$\r\n\r\nwhere $\\sigma$ is an activation function such as [GeLU](https://paperswithcode.com/method/gelu). $U$ and $V$ define linear projections along the channel dimension - the same as those in the FFNs of Transformers (e.g., their shapes are $768 \\times 3072$ and $3072 \\times 768$ for $\\text{BERT}_{\\text {base }}$).\r\n\r\nA key ingredient is $s(\\cdot)$, a layer which captures spatial interactions. When $s$ is an identity mapping, the above transformation degenerates to a regular FFN, where individual tokens are processed independently without any cross-token communication. One of the major focuses is therefore to design a good $s$ capable of capturing complex spatial interactions across tokens. This leads to the use of a [Spatial Gating Unit](https://www.paperswithcode.com/method/spatial-gating-unit) which involves a modified linear gating.\r\n\r\nThe overall block layout is inspired by [inverted bottlenecks](https://paperswithcode.com/method/inverted-residual-block), which define $s(\\cdot)$ as a [spatial depthwise convolution](https://paperswithcode.com/method/depthwise-separable-convolution). Note, unlike Transformers, gMLP does not require position embeddings because such information will be captured in $s(\\cdot)$.","description_state":"present","introduced_year":null,"introduced_by":{"title":"Pay Attention to MLPs","paper":"/paper/pay-attention-to-mlps","first_author":"Hanxiao Liu","n_authors":4,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/pay-attention-to-mlps"},"source":{"url":"https://arxiv.org/abs/2105.08050v2","title":"Pay Attention to MLPs","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Image Models","url":"/methods/category/image-models","pwc_aliases":[]}],"n_papers_tagged":7,"archive_num_papers":7,"papers_newest_first":[{"paper":"/paper/gswin-gated-mlp-vision-model-with","title":"gSwin: Gated MLP Vision Model with Hierarchical Structure of Shifted Window","date":"2022-08-24","arxiv_id":"2208.11718","n_code_links":0,"syntology":null},{"paper":"/paper/a-proposal-of-multi-layer-perceptron-with","title":"A Proposal of Multi-Layer Perceptron with Graph Gating Unit for Graph Representation Learning and its Application to Surrogate Model for FEM","date":"2022-07-11","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/bag-of-words-vs-sequence-vs-graph-vs","title":"Are We Really Making Much Progress in Text Classification? A Comparative Review","date":"2022-04-08","arxiv_id":"2204.03954","n_code_links":1,"syntology":null},{"paper":"/paper/efficient-language-modeling-with-sparse-all","title":"Efficient Language Modeling with Sparse all-MLP","date":"2022-03-14","arxiv_id":"2203.06850","n_code_links":0,"syntology":null},{"paper":null,"title":"Convolutional Gated MLP: Combining Convolutions & gMLP","date":"2021-11-06","arxiv_id":"2111.03940","n_code_links":0,"syntology":null},{"paper":"/paper/cyclemlp-a-mlp-like-architecture-for-dense","title":"CycleMLP: A MLP-like Architecture for Dense Prediction","date":"2021-07-21","arxiv_id":"2107.10224","n_code_links":8,"syntology":{"ran":8,"of":15,"unverified":7,"pointer_only":1}},{"paper":"/paper/pay-attention-to-mlps","title":"Pay Attention to MLPs","date":"2021-05-17","arxiv_id":"2105.08050","n_code_links":20,"syntology":{"ran":34,"of":44,"unverified":10,"pointer_only":11}}],"papers_shown":7,"tasks":[{"task":"/task/image-classification","name":"Image Classification","papers":3},{"task":"/task/instance-segmentation","name":"Instance Segmentation","papers":2},{"task":"/task/object-detection","name":"Object Detection","papers":2},{"task":"/task/question-answering","name":"Question Answering","papers":2},{"task":"/task/semantic-segmentation","name":"Semantic Segmentation","papers":2},{"task":"/task/object-detection-1","name":"object-detection","papers":2},{"task":"/task/all","name":"All","papers":1},{"task":"/task/classification-1","name":"Classification","papers":1},{"task":"/task/common-sense-reasoning","name":"Common Sense Reasoning","papers":1},{"task":"/task/decoder","name":"Decoder","papers":1},{"task":"/task/deep-learning","name":"Deep Learning","papers":1},{"task":"/task/graph-representation-learning","name":"Graph Representation Learning","papers":1},{"task":"/task/in-context-learning","name":"In-Context Learning","papers":1},{"task":"/task/language-modeling","name":"Language Modeling","papers":1},{"task":"/task/language-modelling","name":"Language Modelling","papers":1},{"task":"/task/mixture-of-experts","name":"Mixture-of-Experts","papers":1},{"task":"/task/multi-label-text-classification-1","name":"Multi Label Text Classification","papers":1},{"task":"/task/multi-label-classification","name":"Multi-Label Classification","papers":1},{"task":"/task/multi-label-text-classification","name":"Multi-Label Text Classification","papers":1},{"task":"/task/natural-language-inference","name":"Natural Language Inference","papers":1}],"tasks_shown":20,"n_tasks":31,"usage_by_year":[{"year":"2021","papers":3},{"year":"2022","papers":4}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/gmlp"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}