Do You Even Need Attention? A Stack of Feed-Forward Layers Does Surprisingly Well on ImageNet

TL;DR

This paper investigates whether attention mechanisms are essential in vision transformers by replacing attention layers with feed-forward layers, revealing that non-attention components significantly contribute to their strong performance on ImageNet.

Contribution

The study demonstrates that a feed-forward-only architecture can achieve competitive accuracy, challenging the assumption that attention is crucial for vision transformer success.

Findings

Feed-forward-only models reach 74.9% top-1 accuracy on ImageNet.

Attention layers may not be the primary factor in vision transformer performance.

Other components like patch embedding could be more influential.

Abstract

The strong performance of vision transformers on image classification and other vision tasks is often attributed to the design of their multi-head attention layers. However, the extent to which attention is responsible for this strong performance remains unclear. In this short report, we ask: is the attention layer even necessary? Specifically, we replace the attention layer in a vision transformer with a feed-forward layer applied over the patch dimension. The resulting architecture is simply a series of feed-forward layers applied over the patch and feature dimensions in an alternating fashion. In experiments on ImageNet, this architecture performs surprisingly well: a ViT/DeiT-base-sized model obtains 74.9\% top-1 accuracy, compared to 77.9\% and 79.9\% for ViT and DeiT respectively. These results indicate that aspects of vision transformers other than attention, such as the patch embedding, may be more responsible for their strong performance than previously thought. We hope these results prompt the community to spend more time trying to understand why our current models are as effective as they are.