Squeeze-and-Excitation (SE) Module: Enhancing CNN Performance by Adaptive Channel Attention

The SE (Squeeze-and-Excitation) module is designed to enhance the performance of convolutional neural networks by learning the importance of each channel in the feature maps. This is achieved by adaptively adjusting the weights of each channel, giving more attention to the important ones. The SE module then performs a weighted average of the features of each channel based on the learned weights, resulting in a final feature representation that is optimized for the task at hand. Overall, the SE module helps to improve the accuracy and efficiency of CNNs by allowing them to focus on the most relevant information in the input data.