One of the other unique features of a CNN is that many neurons can share the same vector of weights and biases, or more formally, the same filter. Why is that important? Because each neuron computes an output value by applying a function to the input values of the previous layer. Incremental adjustments to these weights and biases are what helps the network to learn. If the same filter can be re-used, then the required memory footprint will be greatly reduced. This becomes very important, especially as the image or receptive field gets larger.
CNNs have the following distinguishing features:
- Three-dimensional volumes of neurons: The layers of a CNN have neurons arranged in three dimensions: width, height, and depth. The neurons inside each layer are connected to a small region of the layer before it called their receptive field. Different types of connected layers are...