  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.
