Receptive Field

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  1. Real receptive field is smaller than theorical receptive field, and shrinks by $\frac{1}{\sqrt{n}}$ with $n$ being the number of layers.

  2. Advanced networks (e.g., ResNet) have larger receptive field than old networks (e.g., AlexNet). In latest networks, the receptive field of each pixel in the last layer is as large as the whole image. Generally, larger receptive field leads to higher accuracy, but is not the only factor that influences the accuracy.

Fomoro: a website to calculate receptive field.

Distill: mathematical derivations and open-source library to compute receptive field.

Reference

  1. Wenjie Luo, Yujia Li, Raquel Urtasun, Richard S. Zemel:
    Understanding the Effective Receptive Field in Deep Convolutional Neural Networks. NIPS, 2016.