“You said pooling operations in Convolutional Neural Networks (CNNs) are like the magical zoom-out buttons.”
“They reduce the size of feature maps while keeping the juicy bits of information. But how?” Peter asked.
“There are several types of pooling operations. MAX POOLING, for example, picks the maximum value from each patch of the feature map.”
“It keeps the strongest features—like the boldest edges or brightest spots.”
“Average pooling computes the average of values in each patch. It smooths things out and gives a more generalized view”
“Ok. But averaging also means it is more sensitive to noise than max pooling.”
“Yes. So, you choose to get the sharpest, the best,… or smoothing out signals with averaging”
“And then global pooling applies pooling over the entire feature map instead of small patches. It’s often used before fully connected layers to flatten data.”
“Ok. That sounds like summarizing an entire novel in one powerful sentence.”
“Yes. You need that for extracting information to make a decision.” Professor Owl said.