Squeeze-and-Excitation Networks – Towards Data Science – Medium

Squeeze-and-Excitation Networks (SENets) introduce a building block for CNNs that improves channel interdependencies at almost no computational cost. They were used at this years ImageNet competition and helped to improve the result from last year by 25%. Besides this huge performance boost, they can be easily added to existing architectures. As simple as it may sound, this is it. So, let's take a closer look at why this works so well and how we can potentially improve any model with five simple lines of code.