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Methods › Computer Vision › Image Models › MLP-Mixer

MLP-Mixer

96 papers tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

The MLP-Mixer architecture (or “Mixer” for short) is an image architecture that doesn't use convolutions or self-attention. Instead, Mixer’s architecture is based entirely on multi-layer perceptrons (MLPs) that are repeatedly applied across either spatial locations or feature channels. Mixer relies only on basic matrix multiplication routines, changes to data layout (reshapes and transpositions), and scalar nonlinearities.

It accepts a sequence of linearly projected image patches (also referred to as tokens) shaped as a “patches × channels” table as an input, and maintains this dimensionality. Mixer makes use of two types of MLP layers: channel-mixing MLPs and token-mixing MLPs. The channel-mixing MLPs allow communication between different channels; they operate on each token independently and take individual rows of the table as inputs. The token-mixing MLPs allow communication between different spatial locations (tokens); they operate on each channel independently and take individual columns of the table as inputs. These two types of layers are interleaved to enable interaction of both input dimensions.

Source: MLP-Mixer: An all-MLP Architecture for Vision

Papers archive 2025-07-28

30 shown of 96, 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.

Tasks archive 2025-07-28

20 shown of 138 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Image Classification23
image-classification16
Inductive Bias9
Object Detection7
Semantic Segmentation7
Time Series6
object-detection6
Classification5
Medical Image Analysis4
Transfer Learning4
All3
Benchmarking3
Computational Efficiency3
Data Augmentation3
GPU3
Image-to-Image Translation3
Knowledge Distillation3
Language Modeling3
Language Modelling3
Time Series Forecasting3

Usage over time archive 2025-07-28

Papers per year tagged with MLP-Mixer: 2021 to 2025, peak 26 26 0 2021: 26 papers 2021 2022: 21 papers 2022 2023: 17 papers 2023 2024: 24 papers 2024 2025: 8 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (96 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Image Models

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