Squeeze-and-Excitation Block

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".