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Convolutional Neural Networks (CNNs) are the go-to model for computer vision.Recently, attention-based networks, such as the Vision Transformer, have alsobecome popular. In this paper we show that while convolutions and attention areboth sufficient for good performance, neither of them are necessary. We presentMLP-Mixer, an architecture based exclusively on multi-layer perceptrons (MLPs).MLP-Mixer contains two types of layers: one with MLPs applied independently toimage patches (i.e. "mixing" the per-location features), and one with MLPsapplied across patches (i.e. "mixing" spatial information). When trained onlarge datasets, or with modern regularization schemes, MLP-Mixer attainscompetitive scores on image classification benchmarks, with pre-training andinference cost comparable to state-of-the-art models. We hope that theseresults spark further research beyond the realms of well established CNNs andTransformers.

MLP-Mixer: An all-MLP Architecture for Vision