The document discusses max pooling in convolutional neural networks. It shows an example of applying max pooling to a feature map generated by a convolutional layer. The max pooling operation reduces the feature map into a pooled feature map by taking the maximum value from each window/region in the feature map. This pooled feature map is then passed to the next layer in the network.

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Image Source: Wikipedia

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Image Source: Wikipedia

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Feature Map
0 1 0 0 0
0 1 1 1 0
1 0 1 2 1
1 4 2 1 0
0 0 1 2 1

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Feature Map
0 1 0 0 0
0 1 1 1 0
1 0 1 2 1
1 4 2 1 0
0 0 1 2 1
Max Pooling
Pooled Feature Map

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Feature Map
0 1 0 0 0
0 1 1 1 0
1 0 1 2 1
1 4 2 1 0
0 0 1 2 1
Max Pooling
1
Pooled Feature Map

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Feature Map
Max Pooling
0 1 0 0 0
0 1 1 1 0
1 0 1 2 1
1 4 2 1 0
0 0 1 2 1
1 1
Pooled Feature Map

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Feature Map
Max Pooling
0 1 0 0 0
0 1 1 1 0
1 0 1 2 1
1 4 2 1 0
0 0 1 2 1
1 1 0
Pooled Feature Map

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Feature Map
0 1 0 0 0
0 1 1 1 0
1 0 1 2 1
1 4 2 1 0
0 0 1 2 1
Max Pooling
1 1 0
4
Pooled Feature Map

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Feature Map
0 1 0 0 0
0 1 1 1 0
1 0 1 2 1
1 4 2 1 0
0 0 1 2 1
Max Pooling
1 1 0
4 2
Pooled Feature Map

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Feature Map
0 1 0 0 0
0 1 1 1 0
1 0 1 2 1
1 4 2 1 0
0 0 1 2 1
Max Pooling
1 1 0
4 2 1
Pooled Feature Map

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Feature Map
0 1 0 0 0
0 1 1 1 0
1 0 1 2 1
1 4 2 1 0
0 0 1 2 1
Max Pooling
1 1 0
4 2 1
0
Pooled Feature Map

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Feature Map
0 1 0 0 0
0 1 1 1 0
1 0 1 2 1
1 4 2 1 0
0 0 1 2 1
Max Pooling
1 1 0
4 2 1
0 2
Pooled Feature Map

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Feature Map
0 1 0 0 0
0 1 1 1 0
1 0 1 2 1
1 4 2 1 0
0 0 1 2 1
Max Pooling
1 1 0
4 2 1
0 2 1
Pooled Feature Map

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Evaluation of Pooling Operations in
Convolutional Architectures for
Object Recognition
By Dominik Scherer et al. (2010)
Link:
http://ais.uni-bonn.de/papers/icann2010_maxpool.pdf
Additional Reading:

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Input Image
Convolutional Layer
0 0 0 0 0 0 0
0 1 0 0 0 1 0
0 0 0 0 0 0 0
0 0 0 1 0 0 0
0 1 0 0 0 1 0
0 0 1 1 1 0 0
0 0 0 0 0 0 0
Pooling Layer
Convolution Pooling

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Image Source: scs.ryerson.ca/~aharley/vis/conv/flat.html