convolutional neural networks fully connected layer can be described as ( )where is the input vectory is the output vectorw is set of weightsand is the activation function correspondinglya convolutional layer can be described as ( ( ))where denotes the convolution operation between the input and the weights let' now contrast the fully connected layer with the convolutional layer figure - illustrates fully connected layerand figure - illustrates convolutional layerschematically figure - illustrates parameter sharing in the convolutional layer and the lack of it in the fully connected layer the following points should be notedfor the same number of inputs and outputsthe fully connected layer has lot more connections and correspondingly weights than convolutional layer the interactions among inputs to produce outputs are fewer in convolutional layers as compared to fully connected layer this is referred to as sparse interactions parameters/weights are shared across the convolutional layergiven that the kernel is much smaller than the input and the kernel slides across the input thusthere are lot fewer unique parametersweights in convolutional layer