Original article was published by Manik Soni on Deep Learning on Medium
Max Pooling in Convolutional Neural Network(CNN)
Max Pooling is one of the steps in building a Convolutional Neural Network(CNN)
Max Pooling helps to reduce the feature map in order to do the classification more precisely. Let’s take an example to understand this topic better.
Example: Consider the ‘cheetah’ image.
You can take different side video of different ‘cheetahs’
But we want the machine to understand in the same way, that is extracting a common feature from already extracted feature map which helps to do the categorization that the given animal is a ‘cheetahs’
This concept is known as Max Pooling.
How does Max Pooling work?
Let us suppose there is a Feature Map,
Now, we are doing the pooling of the above feature.
Now, Reducing the Feature Map into Pooled Feature Map. Make a 2*2 matrix and take a maximum from the 2*2 matrix.
Again, mapping the pooled feature
Do this mapping until the end.
Fill the Pooled feature map until the end.
Now each of the feature maps, we are applying max-pooling to get a pooled feature map.
So in the real-life application if, we try to visualize how pooling is applied at the back of Convolutional Neural Network(CNN)?.