ResMLP: Feedforward Networks for Image Classification With Data-Efficient Training

We present ResMLP, an architecture built entirely upon multi-layer perceptrons for image classification. Finally, by adapting our model to machine translation we achieve surprisingly good results.

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• 1 It is a simple residual network that alternates (i) a linear layer in which image patches interact, independently and identically across channels, and (ii) a two-layer feed-forward network in which channels interact independently per patch.
• 2 When trained with a modern training strategy using heavy data-augmentation and optionally distillation, it attains surprisingly good accuracy/complexity trade-offs on ImageNet.
• 3 We also train ResMLP models in a self-supervised setup, to further remove priors from employing a labelled dataset.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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