#64 Machine Learning & Data Science Challenge 64

#64 Machine Learning & Data Science Challenge 64
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What is the pooling operation on CNN?

Pooling Layer:

  • It is commonly used to periodically insert a Pooling layer in-between successive Conv layers in a ConvNet architecture.

  • Its function is to progressively reduce the spatial size of the representation to reduce the number of parameters and computations in the network and hence also control overfitting.

  • The Pooling Layer operates independently on every depth slice of the input and resizes it spatially, using the MAX operation.

  • The most common form is a pooling layer with filters of size 2x2 applied with a stride of 2 downsamples of every depth slice in the input by two along both width and height, discarding 75% of the activations.

  • Every MAX the operation would, in this case, be taking a max over four numbers (little 2x2 region in some depth slice). The depth dimension remains unchanged.