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MLP Mixer Models
These sources collectively explore the **MLP-Mixer architecture** and its numerous extensions across computer vision and audio tasks. The core concept of the Mixer is to separate and blend information—originally via **token-mixing** (spatial locations) and **channel-mixing** (features)—using only **Multi-Layer Perceptrons (MLPs)**, which is seen as a simpler alternative to CNNs and Vision Transformers. One source introduces **KAN-Mixers**, replacing standard MLPs with **Kolmogorov-Arnold Networks (KANs)** to potentially improve accuracy and interpretability for image classification, showing strong results on CIFAR-10. Other works propose structural modifications, such as the **Circulant Channel-Specific (CCS) token-mixing MLP** to improve spatial invariance and efficiency, and **ConvMixer**, which uses large-kernel convolutions for mixing. Furthermore, the Mixer principle is applied to audio classification with **ASM-RH**, which blends **Roll-Time** and **Hermit-Frequency** information, proving the **Mixer is a versatile paradigm** adaptable to domain-specific feature perspectives. Finally, research also suggests that the **success of the MLP-Mixer** is rooted in its effective structure as a **wide and sparse MLP**, which embeds sparsity as an inductive bias.
Sources:
1. KAN-Mixers: a new deep learning architecture for image classification (Excerpts)
https://arxiv.org/html/2503.08939v1
2. MLP-Mixer: An all-MLP Architecture for Vision | https://arxiv.org/pdf/2105.01601
3. ResMLP: Feedforward networks for image classification with data-efficient training | https://arxiv.org/pdf/2105.03404
4. Pay Attention to MLPs (gMLP) | https://arxiv.org/pdf/2105.08050
5. Rethinking Token-Mixing MLP for MLP-based Vision Backbone (CCS Token-Mixing MLP) | https://arxiv.org/pdf/2106.14882
6. Patches Are All You Need? (ConvMixer) | https://arxiv.org/pdf/2201.09792
7. Understanding MLP-Mixer as a Wide and Sparse MLP | https://arxiv.org/pdf/2306.01470
8. Strip-MLP: Efficient Token Interaction for Vision MLP | https://arxiv.org/pdf/2307.11458
9. Mixer is more than just a model (ASM-RH) | https://arxiv.org/pdf/2402.18007
10. DynaMixer: A Vision MLP Architecture with Dynamic Mixing | https://proceedings.mlr.press/v162/wang22i/wang22i.pdf
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By mcgrofThese sources collectively explore the **MLP-Mixer architecture** and its numerous extensions across computer vision and audio tasks. The core concept of the Mixer is to separate and blend information—originally via **token-mixing** (spatial locations) and **channel-mixing** (features)—using only **Multi-Layer Perceptrons (MLPs)**, which is seen as a simpler alternative to CNNs and Vision Transformers. One source introduces **KAN-Mixers**, replacing standard MLPs with **Kolmogorov-Arnold Networks (KANs)** to potentially improve accuracy and interpretability for image classification, showing strong results on CIFAR-10. Other works propose structural modifications, such as the **Circulant Channel-Specific (CCS) token-mixing MLP** to improve spatial invariance and efficiency, and **ConvMixer**, which uses large-kernel convolutions for mixing. Furthermore, the Mixer principle is applied to audio classification with **ASM-RH**, which blends **Roll-Time** and **Hermit-Frequency** information, proving the **Mixer is a versatile paradigm** adaptable to domain-specific feature perspectives. Finally, research also suggests that the **success of the MLP-Mixer** is rooted in its effective structure as a **wide and sparse MLP**, which embeds sparsity as an inductive bias.
Sources:
1. KAN-Mixers: a new deep learning architecture for image classification (Excerpts)
https://arxiv.org/html/2503.08939v1
2. MLP-Mixer: An all-MLP Architecture for Vision | https://arxiv.org/pdf/2105.01601
3. ResMLP: Feedforward networks for image classification with data-efficient training | https://arxiv.org/pdf/2105.03404
4. Pay Attention to MLPs (gMLP) | https://arxiv.org/pdf/2105.08050
5. Rethinking Token-Mixing MLP for MLP-based Vision Backbone (CCS Token-Mixing MLP) | https://arxiv.org/pdf/2106.14882
6. Patches Are All You Need? (ConvMixer) | https://arxiv.org/pdf/2201.09792
7. Understanding MLP-Mixer as a Wide and Sparse MLP | https://arxiv.org/pdf/2306.01470
8. Strip-MLP: Efficient Token Interaction for Vision MLP | https://arxiv.org/pdf/2307.11458
9. Mixer is more than just a model (ASM-RH) | https://arxiv.org/pdf/2402.18007
10. DynaMixer: A Vision MLP Architecture with Dynamic Mixing | https://proceedings.mlr.press/v162/wang22i/wang22i.pdf