Understanding the Effective Receptive Field in Deep Convolutional Neural Networks

Understand

We study characteristics of receptive fields of units in deep convolutional networks.

• The receptive field size is a crucial issue in many visual tasks, as the output must respond to large enough areas in the image to capture information about large objects.
• We introduce the notion of an effective receptive field, and show that it both has a Gaussian distribution and only occupies a fraction of the full theoretical receptive field.
• We analyze the effective receptive field in several architecture designs, and the effect of nonlinear activations, dropout, sub-sampling and skip connections on it.