Squeeze and Excitation Networks -- Idiot Developer

Convolutional Neural Network (CNN) has been most widely used in the field of computer vision and visual perception to solve multiple tasks such as image classification, semantic segmentation and many more. However, there is a need for approaches that can further improve its performance. One such approach is to add some attention mechanism to an already existing CNN architecture for further improvements. Squeeze and Excitation Network (SENet) is one such attention mechanism that is most widely used for performance improvements. In the article, we are going to learn more about the Squeeze and Excitation Networks, how they work and how they help to improve performance. The squeeze and excitation attention mechanism was introduced in the year 2018 by Hu et al. in their paper " Squeeze-and-Excitation Networks " at CVPR 2018 with a journal version in TPAMI.