Understanding the Effective Receptive Field in Deep Convolutional Neural Networks

We study characteristics of receptive fields of units in deep convolutional. The receptive field size is a crucial issue in many visual tasks, as output must respond to large enough areas in the image to capture about large objects. We introduce the notion of an effective field, and show that it both has a Gaussian distribution and only a fraction of the full theoretical receptive field. We analyze the receptive field in several architecture designs, and the effect of activations, dropout, sub-sampling and skip connections on it. This to suggestions for ways to address its tendency to be too small.