Squeeze-and-Excitation Block
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](https://paperswithcode.com/method/average-pooling).
- A dense layer followed by a [ReLU](https://paperswithcode.com/method/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".
Squeeze-and-Excitation Networks
https://paperswithcode.com/paper/squeeze-and-excitation-networks
Squeeze-and-Excitation Networks