  The central building block of convolutional neural networks (CNNs) is the
convolution operator, which enables networks to construct informative features
by fusing both spatial and channel-wise information within local receptive
fields at each layer. A broad range of prior research has investigated the
spatial component of this relationship, seeking to strengthen the
representational power of a CNN by enhancing the quality of spatial encodings
throughout its feature hierarchy. In this work, we focus instead on the channel
relationship and propose a novel architectural unit, which we term the
"Squeeze-and-Excitation" (SE) block, that adaptively recalibrates channel-wise
feature responses by explicitly modelling interdependencies between channels.
We show that these blocks can be stacked together to form SENet architectures
that generalise extremely effectively across different datasets. We further
demonstrate that SE blocks bring significant improvements in performance for
existing state-of-the-art CNNs at slight additional computational cost.
Squeeze-and-Excitation Networks formed the foundation of our ILSVRC 2017
classification submission which won first place and reduced the top-5 error to
2.251%, surpassing the winning entry of 2016 by a relative improvement of ~25%.
Models and code are available at https://github.com/hujie-frank/SENet.
