Computer Vision: MaxPooling and Dropouts
Let’s first have an overview of various elements of a typical CNN layer/operation:
Filter: Also called Kernel or Feature Detector. A small matrix. There can be multiple filters in a single convolutional layer. The same-sized filters are used within a convolutional layer. Each filter has a specific function. Multiple filters are used to identify a different set of features in the image.
The size of the filter and the number of filters are hyperparameters. The elements inside the filter define the filter configuration. These weights of the filter are learned during the training.
Feature map: The feature map stores the output of different convolution operations between the image and the filter(s). This is the input for the next pooling layer. The number of elements in the feature map equals the number of different image sections that we obtained by moving the filter(s) on the image. And number of features equals number of filters used.
MAXPOOLING
Convolutional layers make our network translationally invariant. They look for patterns in the image and record whether they found those the patterns we are after in each part of the image. Thus, they help us detect patterns no matter where they appear in the…
