SAENet

2000년 도입 · 논문 1편에서 사용

This method introduces the aggregated dense block within the squeeze excitation block to enhance representation. The squeeze method compresses the input flow and sends it to excitation with dense layers to regain its shape. The paper introduces multiple dense layers stacked side by side, similar to ResNeXt. This learns global representations from the condensed information which enhances the representational power of the network.

출처: Squeeze aggregated excitation network

소개 논문: Squeeze aggregated excitation network