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Pooling Layer
This layer reduces the dimensions of the input image while still retaining the important features. This will help
in making the input image more resistant to small transformations, distortions and translations. All this is done
to reduce the number of parameters and computation in the network thus making it more manageable and
improving the efficiency of the whole system.
For example, if an image of an animal is given as an input to the CNN then by just retaining the shape of the eyes,
ears and face it is easy to identify an animal. Keeping all the features could increase the processing time and cause
the model to become more complex and prone to overfitting.
There are two types of pooling:
• Max Pooling: Max Pooling is the most commonly used method that selects the maximum value of the current
image view and helps preserve the maximum detected features.
• Average Pooling: Average Pooling finds out the average value of the current image view and thus downsamples
the feature map.
max pooling
25 45
105 86
13 25 45 4
11 19 17 26
36 110 86 10
average pooling
79 115 19 21
17 23
85 34
Max
Pooling
Sum
Only non-negative values
Rectified Feature Map
The pooling layer is important in the CNN as it performs a series of tasks which are as follows:
• Pooling reduces the spatial dimensions (height and width) of the feature map, which helps reduce the number
of parameters and computation in the network. This makes the model faster and more efficient.
• Even though the size is reduced, the most important features (like edges and patterns) are preserved, ensuring
that the network can still make accurate predictions based on the crucial information.
• Pooling helps make the model more robust to variations in the input image (e.g., slight changes in position,
rotation, or scale of objects in the image). Since pooling looks at local regions, it makes the network less
sensitive to small transformations.
354 Touchpad Artificial Intelligence (Ver. 3.0)-X

