What's a convolutional layer?

Neural nets used in the area of computer vision are generally Convolutional Neural Networks(CNN’s). You can learn about convolutions below. It appears that convolutions are quite powerful when it comes to working with images and videos due to their ability to extract and learn complex features. Thus CNN’s are a go-to method for any problem in computer vision.

The idea of the convolutional layer is the assumption that the information needed for making a decision often is spatially close and thus, it only takes the weighted sum over nearby inputs. It also assumes that the networks’ kernels can be reused for all nodes, hence the number of weights can be drastically reduced. To counteract only one feature being learnt per layer, multiple kernels are applied to the input which creates parallel channels in the output. Consecutive layers can also be stacked to allow the network to find more high-level features.

A fully-connected layer needs one weight per inter-layer connection, which means the number of weights which needs to be computed quickly balloons as the number of layers and nodes per layer is increased.

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