We built a tool for calculating the receptive field of convolutional filters:  #deeplearning
  • A convolutional layer operates over a local region of the input to that layer with the size of this local region usually specified directly.
  • You can also compute the effective receptive field of a convolutional layer which is the size of the input region to the network that contributes to a layers’ activations.
  • For example, if the first convolutional layer has a receptive field of 3x3 then it’s effective receptive field is also 3x3 since it operates directly on the input.
  • However if the second layer of a convolutional network also has a 3x3 filter, then it’s (local) receptive field is 3x3, but it’s effective receptive field is 5x5.

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