Convolutional neural networks have spatial representations which read
patterns in the vision tasks. Squeeze and excitation links the channel wise
representations by explicitly modeling on channel level. Multi layer
perceptrons learn global representations and in most of the models it is used
often at the end after all convolutional layers to gather all the information
learned before classification. We propose a method of inducing the global
representations within channels to have better performance of the model. We
propose SaEnet, Squeeze aggregated excitation network, for learning global
channelwise representation in between layers. The proposed module takes
advantage of passing important information after squeeze by having aggregated
excitation before regaining its shape. We also introduce a new idea of having a
multibranch linear(dense) layer in the network. This learns global
representations from the condensed information which enhances the
representational power of the network. The proposed module have undergone
extensive experiments by using Imagenet and CIFAR100 datasets and compared with
closely related architectures. The analyzes results that proposed models
outputs are comparable and in some cases better than existing state of the art
architectures.

本研究提出了一种称为 SaEnet 的方法，该方法利用多层感知机和聚合激活网络构建卷积神经网络的全局通道表示，通过在层之间诱导全局表示，并在网络中引入多分支线性层，从而增强网络的表征能力。通过使用 Imagenet 和 CIFAR100 数据集进行大量实验证明，该模型的性能与现有最先进的架构相当甚至更好。