A guide to receptive field arithmetic for Convolutional Neural Networks

The receptive field is perhaps one of the most important concepts in Convolutional Neural Networks (CNNs) that deserves more attention from the literature. All of the state-of-the-art object recognition methods design their model architectures around this idea. However, to my best knowledge, currently there is no complete guide on how to calculate and visualize the receptive field information of a CNN. This post fills in the gap by introducing a new way to visualize feature maps in a CNN that exposes the receptive field information, accompanied by a complete receptive field calculation that can be used for any CNN architecture. I've also implemented a simple program to demonstrate the calculation so that anyone can start computing the receptive field and gain better knowledge about the CNN architecture that they are working with.