HaloNet

2000년 도입 · 논문 2편에서 사용

A HaloNet is a self-attention based model for efficient image classification. It relies on a local self-attention architecture that efficiently maps to existing hardware with haloing. The formulation breaks translational equivariance, but the authors observe that it improves throughput and accuracies over the centered local self-attention used in regular self-attention. The approach also utilises a strided self-attentive downsampling operation for multi-scale feature extraction.

출처: Scaling Local Self-Attention for Parameter Efficient Visual Backbones

소개 논문: Scaling Local Self-Attention for Parameter Efficient Visual Backbones