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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.




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