- 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.
@jimmfleming: “We built a tool for calculating the receptive field of convolutional filters: #deeplearning” open tweet »