Читать книгу Computational Analysis and Deep Learning for Medical Care - Группа авторов - Страница 22

Hu et al. [8] proposed a Squeeze-and-Excitation Network (SENet) (first position on ILSVRC 2017 category) with lightweight gating mechanism. This architecture focuses explicitly on model interdependencies between the channels of convolutional features and to achieve dynamic channel-wise feature recalibration. In the squeeze phase, SE block uses global average pooling operation and in the excitation phase uses channel-wise scaling. For an input image of size 224 × 224, the running time of ResNet-50 is 164 ms, whereas it is 167 ms for SE-ResNet-50. Also, SE-ResNet-50 requires ∼3.87 GFLOPs, which shows a 0.26% relative increase over the original ResNet-50. The top-5 error is reduced to 2.251%. Figure 1.9 shows the architecture of SE-ResNet, and Table 1.9 shows ResNet and its comparison with SE-ResNet-50 and SE-ResNeXt-50.