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Input Feature Map                                  Rectified Feature Map




                                                                   ReLU






                         Black = negative; white = positive values          Only non-negative values

                 In the resulting feature map after applying ReLU:
                 When the ReLU activation function is applied, it eliminates all negative values, essentially flattening the regions
                 where there is no significant change or where the pixel values are below zero.

                 As a result, positive values are kept, and the transitions between dark and light areas become more defined,
                 enhancing the edges and features in the feature map.

                 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                                    max pooling
                 transformations, distortions and translations. All this is done                               25   45
                 to reduce the number of parameters and computation in the
                                                                                                               105  86
                 network thus making it more manageable and improving       13   25   45    4
                 the efficiency of the whole system.
                                                                            11   19   17   26
                 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,   36  110   86   10
                 ears and face it is easy to identify an animal. Keeping all the                            average pooling
                                                                            79  115   19   21
                 features could increase the processing time and cause the                                     17   23
                 model to become more complex and prone to overfitting.
                                                                                                               85   34
                 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



                                                                               Sum
                                 Only non-negative values

                                       Rectified Feature Map






                                                                                             Computer Vision    219
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