What Is A Feature Map in Convolutional Neural Networks?

 

The feature map is the output of one filter applied to the previous layer. It looks hard, doesn't it? So, let me put it in a simple way.

In convolutional networks (CNN), you look at an image through a smaller window and move that window to the right and down. That way you can find features in that window, for example, a horizontal line or a vertical line or a curve, etc. What exactly a convolutional neural network considers an important feature is defined while learning.

Wherever you find those features, you report that in the feature maps. A certain combination of features in a certain area can signal a larger, more complex feature exists there.

I know it sounds confusing again, so giving you another example in a simple way. For instance, in a 32 × 32 image, dragging the 5 × 5 receptive fields across the input image data with a stride width of 1 will result in a feature map of 28 × 28 (32–5+1 × 32–5+1) output values or 784 distinct activations per image. Hope this answered your question.