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Poster
Understanding the Effective Receptive Field in Deep Convolutional Neural Networks
Wenjie Luo · Yujia Li · Raquel Urtasun · Richard Zemel

Wed Dec 07 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #170
in Wednesday Posters »

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.

Keywords: Deep Learning or Neural Networks
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Author Information

Wenjie Luo (University of Toronto)
Yujia Li (University of Toronto)
Raquel Urtasun (University of Toronto)
Richard Zemel (Columbia University)

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