Explain Receptive Fields in CNN?

Receptive fields are defined portion of space or spatial construct containing units that provide input to a set of units within a corresponding layer. The receptive field is defined by the filter size of a layer within a convolution neural network. The receptive field is also an indication of the extent of the scope of input data a neuron or unit within a layer can be exposed to.

Advantages of local receptive fields in recognizing visual patterns lie in the fact that the units or neurons within a layer are directly tasked with learning visual features from a small region of the input data — this isn’t the case in fully connected neural networks, where a unit receives input from units within the previous layer.

In the lower layers within a CNN, the units/neurons learn low-level features within the image such as lines, edges, contours etc. The higher layers learn more abstract features of the image such as shapes, since the region of the image a unit within a higher layer is exposed to is larger as a result of the accumulated receptive fields of previous lower layers.