Image Model Blocks

Squeeze-and-Excitation Block

Introduced by Hu et al. in Squeeze-and-Excitation Networks

The Squeeze-and-Excitation Block is an architectural unit designed to improve the representational power of a network by enabling it to perform dynamic channel-wise feature recalibration. The process is:

  • The block has a convolutional block as an input.
  • Each channel is "squeezed" into a single numeric value using average pooling.
  • A dense layer followed by a ReLU adds non-linearity and output channel complexity is reduced by a ratio.
  • Another dense layer followed by a sigmoid gives each channel a smooth gating function.
  • Finally, we weight each feature map of the convolutional block based on the side network; the "excitation".
Source: Squeeze-and-Excitation Networks

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