Image Model Blocks

MLP-Mixer Layer

Introduced by Tolstikhin et al. in MLP-Mixer: An all-MLP Architecture for Vision

A Mixer layer is a layer used in the MLP-Mixer architecture proposed by Tolstikhin et. al (2021) for computer vision. Mixer layers consist purely of MLPs, without convolutions or attention. It takes an input of embedded image patches (tokens), with its output having the same shape as its input, similar to that of a Vision Transformer encoder. As suggested by its name, Mixer layers "mix" tokens and channels through its "token mixing" and "channel mixing" MLPs contained the layer. It utilizes previous techniques by other architectures, such as layer normalization, skip-connections, and regularization methods.

Image credit: Tolstikhin, I. O., Houlsby, N., Kolesnikov, A., Beyer, L., Zhai, X., Unterthiner, T., ... & Dosovitskiy, A. (2021). Mlp-mixer: An all-mlp architecture for vision. Advances in Neural Information Processing Systems, 34, 24261-24272.

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

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Image Classification 1 100.00%

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