Sequeeze-and-Excitation(SE) Block是一个子模块,可以嵌到其他的模型中,作者采用SENet Block和ResNeXt结合在ILSVRC 2017的分类项目中得了第一。 层次结构 Sequeeze-and-Excitation的层次结构如下 1、AdaptiveAvgPool2d 2、Linear 3、ReLU 4、Linear 5、Sigmoid 先拆成两部分Squeeze部分和Excitation部分 Squeeze 部分就是...
Sequeeze-and-Excitation是什么 Sequeeze-and-Excitation(SE) Block是一个子模块,可以嵌到其他的模型中,作者采用SENet Block和ResNeXt结合在ILSVRC 2017的分类项目中得了第一。 层次结构 Sequeeze-and-Excitation的层次结构如下 1、AdaptiveAvgPool2d 2、Linear 3、ReLU 4、Linear 5、Sigmoid 先拆成两部分Squeeze部分和...
The block is simple to implement in Keras. It composes of a GlobalAveragePooling2D, 2 Dense blocks and an elementwise multiplication. Shape inference can be done automatically in Keras. It can be imported fromse.py. fromtensorflow.keras.layersimportGlobalAveragePooling2D,Reshape,Dense,Permute,mult...
Subsequent to ResNet, Squeeze and Excitation Network (SENet) introduced a squeeze and excitation block (SE block) on every residual mapping of ResNet to improve its performance. The SE block quantifies the importance of each feature map and weights them accordingly. In this work, we propose ...