Skip to yearly menu bar Skip to main content


Understanding the Effective Receptive Field in Deep Convolutional Neural Networks

Wenjie Luo · Yujia Li · Raquel Urtasun · Richard Zemel

Area 5+6+7+8 #170

Keywords: [ Deep Learning or Neural Networks ]


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 size, and show that it both has a Gaussian distribution and only occupies a fraction of the full theoretical receptive field size. We analyze the effective receptive field in several architecture designs, and the effect of sub-sampling, skip connections, dropout and nonlinear activations on it. This leads to suggestions for ways to address its tendency to be too small.

Live content is unavailable. Log in and register to view live content