Figure 1: Two ways to visualize CNN feature maps. In all cases, we uses the convolution C with kernel size k = 3x3, padding size p = 1x1, stride s = 2x2. (Top row) Applying the convolution on a 5x5 input map to produce the 3x3 green feature map. (Bottom row) Applying the same convolution on top of the green feature map to produce the 2x2 orange feature map. (Left column) The common way to visualize a CNN feature map. Only looking at the feature map, we do not know where a feature is looking at (the center location of its receptive field) and how big is that region (its receptive field size). It will be impossible to keep track of the receptive field information in a deep CNN. (Right column) The fixed-sized CNN feature map visualization, where the size of each feature map is fixed, and the feature is located at the center of its receptive field.
Figure 2: Another fixed-sized CNN feature map representation. The same convolution C is applied on a bigger input map with i = 7x7. I drew the receptive field bounding box around the center feature and removed the padding grid for a clearer view. The fixed-sized CNN feature map can be presented in 3D (Left) or 2D (Right).
Figure 3: Applying the receptive field calculation on the example given in Figure 1. The first row shows the notations and general equations, while the second and the last row shows the process of applying it to calculate the receptive field of the output layer given the input layer information.

ML Review

Highlights from Machine Learning Research, Projects and Learning Materials. From and For ML Scientists, Engineers an Enthusiasts.

Dang Ha The Hien

Written by

Software Engineer at Google

ML Review

ML Review

Highlights from Machine Learning Research, Projects and Learning Materials. From and For ML Scientists, Engineers an Enthusiasts.

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