Squeeze-and-Excitation Networks

@article{Hu2017SqueezeandExcitationN,
  title={Squeeze-and-Excitation Networks},
  author={Jie Hu and Li Shen and Samuel Albanie and Gang Sun and Enhua Wu},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2017},
  volume={42},
  pages={2011-2023}
}
  • Jie HuLi Shen E. Wu
  • Published 5 September 2017
  • Computer Science
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
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… 
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