Convolution Neural Networks (CNNs)

What are CNNs?

Convolution Neural Networks (CNNs) are a deep learning technique that is used for image classification, object detection, and segmentation. There are three main points to understanding how a CNN works. These include: local receptive fields, shared weights and biases, and activation pooling.

Local Receptive Fields

  • All neural networks have a hidden layer. These transform input layers into output layers. We do not need to know exactly what the hidden layer is. It is usually pre-trained and considered a black-box object.

  • In a CNN, a small region of input connects to the neurons in the hidden layer. These regions are called local receptive fields.

  • The local receptive field is translated using convolution to create a feature map of the images passed through.

  • Below is an image explaining the concept of hidden layers.