What is a receptive field?

A receptive field is the area of input data that a specific neuron in a neural network responds to, especially in convolutional neural networks (CNNs).

Simply put, the receptive field determines which part of the original image or signal the neuron 'sees' when computing its output.

For example, in a convolutional layer with a 3x3 kernel, the receptive field of a neuron is 3x3 pixels of the input image. When multiple layers are applied sequentially, the receptive field grows, and the neuron begins to consider broader contexts.

This is important for capturing local and global features in data, such as recognizing objects in images.

Example: if the first CNN layer looks at 3x3 pixels, and the second layer looks at the 3x3 output of the first layer, then the receptive field of the second layer will be 5x5 pixels of the original image (considering overlaps and stride of the convolution).