Global average pooling is a down-sampling technique used in convolutional neural networks (CNNs) that reduces the spatial dimensions of feature maps by taking the average of all values in each feature map. This method replaces the traditional fully connected layers, leading to fewer parameters and reduced overfitting. It simplifies the architecture and retains important spatial information, which is especially relevant in popular CNN architectures.
congrats on reading the definition of Global Average Pooling. now let's actually learn it.