  Recent work has shown that self-attention can serve as a basic building block
for image recognition models. We explore variations of self-attention and
assess their effectiveness for image recognition. We consider two forms of
self-attention. One is pairwise self-attention, which generalizes standard
dot-product attention and is fundamentally a set operator. The other is
patchwise self-attention, which is strictly more powerful than convolution. Our
pairwise self-attention networks match or outperform their convolutional
counterparts, and the patchwise models substantially outperform the
convolutional baselines. We also conduct experiments that probe the robustness
of learned representations and conclude that self-attention networks may have
significant benefits in terms of robustness and generalization.
