Summary of Key Features

The key features of CNN can be better understood in contrast to traditional multilayer neural networks. The following features are thought to be unique of CNN.

• Local connectivity: Mimic visual cortexes containing neurons that individually respond to small regions of the visual field. It permits location-invariant features to be extracted at lower level layers, which can be assembled to understand complex features in larger scales.

• Shared weights: It reduces the number of parameters to be learned and make the network more scalable and easier to train.

• Multiple feature maps: Allow a rich set of features to be captured at different levels and scales.

• Pooling: Often referred to as down-sampling, which results in a lower resolution version of the feature maps that are more robust to changes in the position of the features in the original image. It also captures the feature structure at a larger scale.