Feature maps (Activation maps)

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Case study notes

Introduction

The feature map is the output of one filter applied to the previous layer. A given filter is drawn across the entire previous layer, moved one pixel at a time. Each position results in an activation of the neuron and the output is collected in the feature map. You can see that if the receptive field is moved one pixel from activation to activation, then the field will overlap with the previous activation by (field width - 1) input values.

 <ref> https://www.quora.com/What-is-meant-by-feature-maps-in-convolutional-neural-networks </ref>
 

How does it work or a deeper look

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Examples

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Pictures, diagrams

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External links

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