Pooling

Pooling is an operation used in Convolutional Neural Networks (CNNs) to reduce the dimensionality of feature maps, thereby reducing the computational complexity of the model. By summarizing the features in a local neighborhood, pooling also provides a form of translation invariance, making the network more robust to variations in the input.

There are several types of pooling operations:

1. Max Pooling: This is the most commonly used pooling operation. It takes the maximum value from the feature map within the pooling window. Max pooling is effective in capturing the most prominent feature in a local region. It is done for getting sharpest features.
2. Average Pooling: This operation calculates the average value of the elements within the pooling window. Average pooling is useful when all features are equally important and we want to maintain the overall characteristics of the local region. It is done for getting smooth features.
3. Sum Pooling: Sum pooling calculates the sum of all elements in the pooling window. This is less commonly used compared to max and average pooling.

These operations are typically used after convolutional layers in a CNN. They help to make the representation smaller and more manageable, and to reduce overfitting by providing an abstracted form of the representation.